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[Context and Motivation] Content-based recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a new requirement is proposed by a stakeholder, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn, identify previously developed code. [Question/problem] Several NLP approaches for similarity computation are available, and there is little empirical evidence on the adoption of an effective technique in recommender systems specifically oriented to requirements-based code reuse. [Principal ideas/results] This study compares different state-of-the-art NLP approaches and correlates the similarity among requirements with the similarity of their source code. The evaluation is conducted on real-world requirements from two industrial projects in the railway domain. Results show that requirements similarity computed with the traditional tf-idf approach has the highest correlation with the actual software similarity in the considered context. Furthermore, results indicate a moderate positive correlation with Spearman's rank correlation coefficient of more than 0.5. [Contribution] Our work is among the first ones to explore the relationship between requirements similarity and software similarity. In addition, we also identify a suitable approach for computing requirements similarity that reflects software similarity well in an industrial context. This can be useful not only in recommender systems but also in other requirements engineering tasks in which similarity computation is relevant, such as tracing and categorization.

Recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a stakeholder proposes a new requirement, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn, identify previously developed code. Several NLP approaches for similarity computation between requirements are available. However, there is little empirical evidence on their effectiveness for code retrieval. This study compares different NLP approaches, from lexical ones to semantic, deep-learning techniques, and correlates the similarity among requirements with the similarity of their associated software. The evaluation is conducted on real-world requirements from two industrial projects from a railway company. Specifically, the most similar pairs of requirements across two industrial projects are automatically identified using six language models. Then, the trace links between requirements and software are used to identify the software pairs associated with each requirements pair. The software similarity between pairs is then automatically computed with JPLag. Finally, the correlation between requirements similarity and software similarity is evaluated to see which language model shows the highest correlation and is thus more appropriate for code retrieval. In addition, we perform a focus group with members of the company to collect qualitative data. Results show a moderately positive correlation between requirements similarity and software similarity, with the pre-trained deep learning-based BERT language model with preprocessing outperforming the other models. Practitioners confirm that requirements similarity is generally regarded as a proxy for software similarity. However, they also highlight that additional aspect comes into play when deciding software reuse, e.g., domain/project knowledge, information coming from test cases, and trace links. Our work is among the first ones to explore the relationship between requirements and software similarity from a quantitative and qualitative standpoint. This can be useful not only in recommender systems but also in other requirements engineering tasks in which similarity computation is relevant, such as tracing and change impact analysis.

Requirements prioritization plays an important role in driving project success during software development. Literature reveals that existing requirements prioritization approaches ignore vital factors such as interdependency between requirements. Existing requirements prioritization approaches are also generally time-consuming and involve substantial manual effort. Besides, these approaches show substantial limitations in terms of the number of requirements under consideration. There is some evidence suggesting that models could have a useful role in the analysis of requirements interdependency and their visualization, contributing towards the improvement of the overall requirements prioritization process. However, to date, just a handful of studies are focused on model-based strategies for requirements prioritization, considering only conflict-free functional requirements. This paper uses a meta-model-based approach to help the requirements analyst to model the requirements, stakeholders, and inter-dependencies between requirements. The model instance is then processed by our modified PageRank algorithm to prioritize the given requirements. An experiment was conducted, comparing our modified PageRank algorithm’s efficiency and accuracy with five existing requirements prioritization methods. Besides, we also compared our results with a baseline prioritized list of 104 requirements prepared by 28 graduate students. Our results show that our modified PageRank algorithm was able to prioritize the requirements more effectively and efficiently than the other prioritization methods.

The software system controlling a train is typically deployed on various hardware architectures and is required to process various signals across those deployments. Increases of such customization scenarios, as well as the needed adherence of the software to various safety standards in different application domains, has led to the adoption of product line engineering within the railway domain. This paper explores the current state-of-practice of software product line development within a team developing industrial embedded software for a train propulsion control system. Evidence is collected by means of a focus group session with several engineers and through inspection of archival data. We report several benefits and challenges experienced during product line adoption and deployment. Furthermore, we identify and discuss research opportunities, focusing in particular on the areas of product line evolution and test automation.

Categorizing existing test specifications can provide insights on coverage of the test suite to extra-functional properties. Manual approaches for test categorization can be time-consuming and prone to error. In this short paper, we propose a semi-automated approach for semantic keywords-based textual test categorization for extra-functional properties. The approach is the first step towards coverage-based test case selection based on extra-functional properties. We report a preliminary evaluation of industrial data for test categorization for safety aspects. Results show that keyword-based approaches can be used to categorize tests for extra-functional properties and can be improved by considering contextual information of keywords.

This tutorial explores requirements-based reuse recommendation for product line assets in the context of clone-and-own product lines.

Software product lines (SPLs) are based on reuse rationale to aid quick and quality delivery of complex products at scale. Deriving a new product from a product line requires reuse analysis to avoid redundancy and support a high degree of assets reuse. In this paper, we propose and evaluate automated support for recommending SPL assets that can be reused to realize new customer requirements. Using the existing customer requirements as input, the approach applies natural language processing and clustering to generate reuse recommendations for unseen customer requirements in new projects. The approach is evaluated both quantitatively and qualitatively in the railway industry. Results show that our approach can recommend reuse with 74% accuracy and 57.4% exact match. The evaluation further indicates that the recommendations are relevant to engineers and can support the product derivation and feasibility analysis phase of the projects. The results encourage further study on automated reuse analysis on other levels of abstractions.

Concurrent programming puts demands on software debugging and testing, as concurrent software may exhibit problems not present in sequential software, e.g., deadlocks and race conditions. In aiming to increase efficiency and effectiveness of debugging and bug-fixing for concurrent software, a deep understanding of concurrency bugs, their frequency and fixingtimes would be helpful. Similarly, to design effective tools and techniques for testing and debugging concurrent software understanding the differences between non-concurrency and concurrency bugs in real-word software would be useful.

Many test prioritisation techniques have been proposed in order to improve test effectiveness of Continuous Integration (CI) pipelines. Particularly, diversity-based testing (DBT) has shown promising and competitive results to improve test effectiveness. We report on a case study considering the CI pipeline of Axis Communications in Sweden. We compared three different prioritisation approaches (i.e., diversity, failure history and time) in terms of their impact on coverage, failure detection rates and reduction on test execution time. Our results reveal that DBT is the best candidate to provide feature coverage, whereas failure rate prioritisation yields better failure coverage. Time-based prioritisation is not a reliable approach to provide cost-effective testing. Moreover, DBT would allow stakeholders to receive quick feedback on many combinations of integrated features to verify their code changes. Our participants report that developers are mainly interested in: (i) receiving quick feedback on a high combination of integrated features to verify their code changes, and (ii) associate their test suites to confidence scores representing the risk of missing failures given that fewer tests are executed.

Hyper-heuristic is a new methodology for the adaptive hybridization of meta-heuristic algorithms to derive a general algorithm for solving optimization problems. This work focuses on the selection type of hyper-heuristic, called the exponential Monte Carlo with counter (EMCQ). Current implementations rely on the memory-less selection that can be counterproductive as the selected search operator may not (historically) be the best performing operator for the current search instance. Addressing this issue, we propose to integrate the memory into EMCQ for combinatorial t-wise test suite generation using reinforcement learning based on the Q-learning mechanism, called Q-EMCQ. The limited application of combinatorial test generation on industrial programs can impact the use of such techniques as Q-EMCQ. Thus, there is a need to evaluate this kind of approach against relevant industrial software, with a purpose to show the degree of interaction required to cover the code as well as finding faults. We applied Q-EMCQ on 37 real-world industrial programs written in Function Block Diagram (FBD) language, which is used for developing a train control management system at Bombardier Transportation Sweden AB. The results show that Q-EMCQ is an efficient technique for test case generation. Addition- ally, unlike the t-wise test suite generation, which deals with the minimization problem, we have also subjected Q-EMCQ to a maximization problem involving the general module clustering to demonstrate the effectiveness of our approach. The results show the Q-EMCQ is also capable of outperforming the original EMCQ as well as several recent meta/hyper-heuristic including modified choice function, Tabu high-level hyper-heuristic, teaching learning-based optimization, sine cosine algorithm, and symbiotic optimization search in clustering quality within comparable execution time. 

In this paper, we apply model-based testing (MBT) to automate the creation of hardware-in-loop (HIL) test cases. In order to select MBT tools, different tools' properties were compared to each other through a literature study, with the result of selecting GraphWalker and MoMuT tools to be used in an industrial case study. The results show that the generated test cases perform similarly to their manual counterparts regarding how the test cases achieved full requirements coverage. When comparing the effort needed for applying the methods, a comparable effort is required for creating the first iteration, while with every subsequent update, MBT will require less effort compared to the manual process. Both methods achieve 100% requirements coverage, and since manual tests are created and executed by humans, some requirements are favoured over others due to company demands, while MBT tests are generated randomly. In addition, a comparison between the used tools showcased the differences in the models' design and their test case generation. The comparison showed that GraphWalker has a more straightforward design method and is better suited for smaller systems, while MoMuT can handle more complex systems but has a more involved design method.

Software systems that are embedded in autonomous Cyber-Physical Systems (CPSs) usually have a large life-cycle, both during its development and in maintenance. This software evolves during its life-cycle in order to incorporate new requirements, bug fixes, and to deal with hardware obsolescence. The current process for developing and maintaining this software is very fragmented, which makes developing new software versions and deploying them in the CPSs extremely expensive. In other domains, such as web engineering, the phases of development and operation are tightly connected, making it possible to easily perform software updates of the system, and to obtain operational data that can be analyzed by engineers at development time. However, in spite of the rise of new communication technologies (e.g., 5G) providing an opportunity to acquire Design-Operation Continuum Engineering methods in the context of CPSs, there are still many complex issues that need to be addressed, such as the ones related with hardware-software co-design. Therefore, the process of Design-Operation Continuum Engineering for CPSs requires substantial changes with respect to the current fragmented software development process. In this paper, we build a taxonomy for Design-Operation Continuum Engineering of CPSs based on case studies from two different industrial domains involving CPSs (elevation and railway). This taxonomy is later used to elicit requirements from these two case studies in order to present a blueprint on adopting Design-Operation Continuum Engineering in any organization developing CPSs.

Well-trained software development personnel, in the art and science of software testing, will effectively and efficiently develop quality software products with potentially fewer, less-critical defects. Thus software testing education is considered to be an important part of curricula for a university degree in Computer Science or Information Systems. The objective of this paper is to determine how much dedicated knowledge in the field of software testing is taught within Swedish universities. To achieve this objective, a systematic search of syllabi for software testing-related courses was done. From 25 Swedish universities offering Computer Science (or related) degrees, 14 currently offer dedicated courses in software testing. Some findings include: 32% of the individual courses were offered at the undergraduate level; 28% of the universities offer courses for specialised testing training; and, for the vast majority of the universities, dedicated software testing courses account for about 5% of the total degree credits. While some universities fare better than others, the overall state of academic software testing education in Sweden is limited but promising.

[Context and Motivation] Requirements in tender documents are often mixed with other supporting information. Identifying requirements in large tender documents could aid the bidding process and help estimate the risk associated with the project. [Question/problem] Manual identification of requirements in large documents is a resource-intensive activity that is prone to human error and limits scalability. This study compares various state-of-the-art approaches for requirements identification in an industrial context. For generalizability, we also present an evaluation on a real-world public dataset. [Principal ideas/results] We formulate the requirement identification problem as a binary text classification problem. Various state-of-the-art classifiers based on traditional machine learning, deep learning, and few-shot learning are evaluated for requirements identification based on accuracy, precision, recall, and F1 score. Results from the evaluation show that the transformer-based BERT classifier performs the best, with an average F1 score of 0.82 and 0.87 on industrial and public datasets, respectively. Our results also confirm that few-shot classifiers can achieve comparable results with an average F1 score of 0.76 on significantly lower samples, i.e., only 20% of the data. [Contribution] There is little empirical evidence on the use of large language models and few-shots classifiers for requirements identification. This paper fills this gap by presenting an industrial empirical evaluation of the state-of-the-art approaches for requirements identification in large tender documents. We also provide a running tool and a replication package for further experimentation to support future research in this area.

The base-choice criterion was proposed as a suitable technique for testing software based on its nominal choice of input parameters. Test cases are created based on this strategy by varying the values of one input parameter at a time while keeping the values of the other parameters fixed on the base choice. However, this strategy might not be as effective when used on industrial control software for testing timed behavior. We propose to incorporate time as another parameter when generating and executing tests by defining the timed base-choice coverage criterion. We performed an empirical evaluation using 11 industrial programs written in the IEC 61131-3 programming language. We found that tests generated for timed base-choice criterion show better code coverage (7% improvement) and fault detection (27% improvement) in terms of mutation score than tests satisfying base-choice coverage criterion. The results demonstrate the feasibility of applying timed base-choice criterion for testing industrial control software.

Testing is an important activity in engineering of industrial software. For such software, testing is usually performed manually by handcrafting test suites based on specific design techniques and domain-specific experience. To support developers in testing, different approaches for producing good test suites have been proposed. In the last couple of years combinatorial testing has been explored with the goal of automatically combining the input values of the software based on a certain strategy. Pairwise testing is a combinatorial technique used to generate test suites by varying the values of each pair of input parameters to a system until all possible combinations of those parameters are created. There is some evidence suggesting that these kinds of techniques are efficient and relatively good at detecting software faults. Unfortunately, there is little experimental evidence on the comparison of these combinatorial testing techniques with, what is perceived as, rigorous manually handcrafted testing. In this study we compare pairwise test suites with test suites created manually by engineers for 45 industrial programs. The test suites were evaluated in terms of fault detection, code coverage and number of tests. The results of this study show that pairwise testing, while useful for achieving high code coverage and fault detection for the majority of the programs, is almost as effective in terms of fault detection as manual testing. The results also suggest that pairwise testing is just as good as manual testing at fault detection for 64% of the programs.

Unconscious biases are hard-wired behaviors that influence testers and can set them on an incorrect course of action. If we understand how these biases affect testers' everyday behavior, we can attempt to mitigate them, then develop more effective tools and strategies to help testers avoid the pitfalls of cognitive biases. But to what extent are testers aware that cognitive biases influence their work? This was the primary question we sought to answer in our research. We developed a questionnaire survey, designed to reveal the extent of software testers’ knowledge about cognitive biases, plus their awareness of the influence biases have on testing. We reached out to software professionals working in different environments and gathered valid data from approximately 60 practitioners. Our results suggest that professionals are indeed aware of biases. Specifically, they are aware of preconceptions such as confirmation bias, fixation, and convenience. In addition, optimism, ownership, and blissful ignorance were other common biases. In common with other research, we observed that people tend to identify more cognitive biases in others than in their own judgments and actions, indicating a vulnerability to bias blind spot.

Automated testing is an essential component of Continuous Integration (CI) and Delivery (CD), such as scheduling automated test sessions on overnight builds. That allows stakeholders to execute entire test suites and achieve exhaustive test coverage, since running all tests is often infeasible during work hours, i.e., in parallel to development activities. On the other hand, developers also need test feedback from CI servers when pushing changes, even if not all test cases are executed. In this paper we evaluate similarity-based test case selection (SBTCS) on integration-level tests executed on continuous integration pipelines of two companies. We select test cases that maximise diversity of test coverage and reduce feedback time to developers. Our results confirm existing evidence that SBTCS is a strong candidate for test optimisation, by reducing feedback time (up to 92% faster in our case studies) while achieving full test coverage using only information from test artefacts themselves.

Since the early days of software testing, automatic test generation has been suggested as a way of allowing tests to be created at a lower cost. However, industrially useful and applicable tools for automatic test generation are still scarce. As a consequence, the evidence regarding the applicability or feasibility of automatic test generation in industrial practice is limited. This is especially problematic if we consider the use of automatic test generation for industrial safety-critical control systems, such as are found in power plants, airplanes, or trains.

In this thesis, we improve the current state of automatic test generation by developing a technique based on model-checking that works with IEC 61131-3 industrial control software. We show how automatic test generation for IEC 61131-3 programs, containing both functional and timing information, can be solved as a model checking problem for both code and mutation coverage criteria. 

The developed technique has been implemented in the CompleteTest tool. To evaluate the potential application of our technique, we present several studies where the tool is applied to industrial control software. Results show that CompleteTest is viable for use in industrial practice; it is efficient in terms of the time required to generate tests that satisfy both code and mutation coverage and scales well for most of the industrial programs considered.

However, our results also show that there are still challenges associated with the use of automatic test generation. In particular, we found that while automatically generated tests, based on code coverage, can exercise the logic of the software as well as tests written manually, and can do so in a fraction of the time, they do not show better fault detection compared to manually created tests. Specifically, it seems that manually created tests are able to detect more faults of certain types (i.e, logical replacement, negation insertion and timer replacement) than automatically generated tests. To tackle this issue, we propose an approach for improving fault detection by using mutation coverage as a test criterion. We implemented this approach in the CompleteTest tool and used it to evaluate automatic test generation based on mutation testing. While the resulting tests were more effective than automatic tests generated based on code coverage, in terms of fault detection, they still were not better than manually created tests.

In summary, our results highlight the need for improving the goals used by automatic test generation tools. Specifically, fault detection scores could be increased by considering some new mutation operators as well as higher-order mutations. Our thesis suggests that automatically generated test suites are significantly less costly in terms of testing time than manually created test suites. One conclusion, strongly supported by the results of this thesis, is that automatic test generation is efficient but currently not quite as effective as manual testing. This is a significant progress that needs to be further studied; we need to consider the implications and the extent to which automatic test generation can be used in the development of reliable safety-critical systems.

Software testing becomes more complex, more time-consuming, and more expensive. The risk that software errors remain undetected and cause critical failures increases. Consequently, in safety-critical development, testing software is standardized and it requires an engineer to show that tests fully exercise, or cover, the logic of the software. This method often requires a trained engineer to perform manual test generation, is prone to human error, and is expensive or impractical to use frequently in production. To overcome these issues, software testing needs to be performed earlier in the development process, more frequently, and aided by automated tools.

We devised an automated test generation tool called COMPLETETEST that avoids many of those problems. The method implemented in the tool and described in this thesis, works with software written in Function Block Diagram language, and can provide tests in just a few seconds. In addition, it does not rely on the expertise of a researcherspecialized in automated test generation and model checking. Although COMPLETETEST itself uses a model checker, a complex technique requiring a high level of expertise to generate tests, it provides a straightforward tabular interface to the intended users. In this way, its users do not need to learn the intricacies of using this approach such as how coverage criteria can be formalized and used by a model checker to automatically generate tests. If the technique can be demonstrated to work in production, it could detect and aid in the detection of errors in safety-critical software development, where conventional testing is not always applicable and efficient.

We conducted studies based on industrial use-case scenarios from Bombardier Transportation AB, showing how the approach can be applied to generate tests in software systems used in the safety-critical domain. To evaluate the approach, it was applied on real-world programs. The results indicate that it is efficient in terms of time required to generate tests and scales well for most of the software. There are still issues to resolve before the technique can be applied to more complex software, but we are already working on ways to overcome them. In particular, we need to understand how its usage in practice can vary depending on human and software process factors.

Software testing is a business-critical process used by private and public organizations and an important source of market competitiveness. Employees of these organizations are facing tough competition and are required to be able to maintain and develop their skills and knowledge in software testing. In the education market, many commercial courses and certifications are available for industrial engineers who wish to improve their skills in software development. Nevertheless, there is a lack of access to world-leading research within the software testing field in these commercial courses that supports the companies’ innovation in software testing. As an alternative, universities are approaching this challenge by developing academic courses on software testing that can suit professionals who need to be able to combine work and studies. This study highlights several good approaches and challenges in developing and teaching three distance web-based software testing courses targeting test practitioners. The proposed approaches for enhancing teaching of software testing in an online setting for industrial practitioners are: active participation at the student’s pace, inclusion of software testing artifacts from the student’s organization as part of assignments, continuous access to online materials, the use of short video materials on testing theory, and setting clear expectations for performing online test design assignments. Finally, several challenges have been identified: poor feedback on assignments, distances between students and teachers, the use of non-realistic assignments and the difficulty for industrial practitioners to complete academic assignments each week. Future work is needed to explore these results in practice, for example on how to shorten distances between students and teachers, as well as how to enhance the inclusion of real-world testing artifacts in course assignments. © Springer Nature Switzerland AG 2020.

When it comes to industrial organizations, current collaboration efforts in software engineering research are very often kept in-house, depriving these organizations off the skills necessary to build independent collaborative research. The current trend, towards empirical software engineering research, requires certain standards to be established which would guide these collaborative efforts in creating a strong partnership that promote independent, evidence-based, software engineering research. This paper examines key enabling factors for an effcient and effective industry-academia collaboration in the software testing domain. A major finding of the research was that while technology is a strong enabler to better collaboration, it must be complemented with industrial openness to disclose research results and the use of a dedicated tooling platform. We use as an example an automated test generation approach that has been developed in the last two years collaboratively with Bombardier Transportation AB in Sweden.

In software development, testers often focus on functional testing to validate implemented programs against their specifications. In safety critical software development, testers are also required to show that tests exercise, or cover, the structure and logic of the implementation. To achieve different types of logic coverage, various program artifacts such as decisions and conditions are required to be exercised during testing. Use of model-checking for structural test generation has been proposed by several researchers. The limited application to models used in practice and the state-space explosion can, however, impact model-checking and hence the process of deriving tests for logic coverage. Thus, there is a need to validate these approaches against relevant industrial systems such that more knowledge is built on how to efficiently use them in practice. In this paper, we present a tool-supported approach to handle software written in the Function Block Diagram language such that logic coverage criteria can be formalized and used by a model-checker to automatically generate tests. To this end, we conducted a study based on industrial use-case scenarios from Bombardier Transportation AB, showing how our toolbox COMPLETETEST can be applied to generate tests in software systems used in the safety-critical domain. To evaluate the approach, we applied the toolbox to 157 programs and found that it is efficient in terms of time required to generate tests that satisfy logic coverage and scales well for most of the programs.

In engineering of safety critical systems, regulatory standards often put requirements on both traceable specification-based testing, and structural coverage on program units. Automated test generation techniques can be used to generate inputs to cover the structural aspects of a program. However, there is no conclusive evidence on how automated test generation compares to manual test design, or how testing based on the program implementation relates to specification-based testing. In this paper, we investigate specification-and implementation-based testing of embedded software written in the IEC 61131-3 language, a programming standard used in many embedded safety critical software systems. Further, we measure the efficiency and effectiveness in terms of fault detection. For this purpose, a controlled experiment was conducted, comparing tests created by a total of twenty-three software engineering master students. The participants worked individually on manually designing and automatically generating tests for two IEC 61131-3 programs. Tests created by the participants in the experiment were collected and analyzed in terms of mutation score, decision coverage, number of tests, and testing duration. We found that, when compared to implementation-based testing, specification-based testing yields significantly more effective tests in terms of the number of faults detected. Specifically, specification-based tests more effectively detect comparison and value replacement type of faults, compared to implementation-based tests. On the other hand, implementation-based automated test generation leads to fewer tests (up to 85% improvement) created in shorter time than the ones manually created based on the specification.

In this paper we present a new testing tool for safety critical applications described in Function Block Diagram (FBD) language aimed to support both a model and a search-based approach. Many benefits emerge from this tool, including the ability to automatically generate test suites from an FBD program in order to comply to quality requirements such as component testing and specific coverage measurements. Search-based testing methods are used to generate test data based on executable code rather than the FBD program, alleviating any problems that may arise from the ambiguities that occur while creating FBD programs. Test cases generated by both approaches are executed and used as a way of cross validation. In the current work, we describe the architecture of the tool, its workflow process, and a case study in which the tool has been applied in a real industrial setting to test a train control management system.

Automated testing tools typically create test cases that are different from what human testers create. This often makes the tools less effective, the created tests harder to understand, and thus results in tools providing less support to human testers. Here, we propose a framework based on cognitive science and, in particular, an analysis of approaches to problem-solving, for identifying cognitive processes of testers. The framework helps map test design steps and criteria used in human test activities and thus to better understand how effective human testers perform their tasks. Ultimately, our goal is to be able to mimic how humans create test cases and thus to design more human-like automated test generation systems. We posit that such systems can better augment and support testers in a way that is meaningful to them.

Growth of software size, lack of resources to perform regression testing, and failure to detect bugs faster have seen increased reliance on continuous integration and test automation. Even with greater hardware and software resources dedicated to test automation, software testing is faced with enormous challenges, resulting in increased dependence on complex mechanisms for automated test case selection and prioritisation as part of a continuous integration framework. These mechanisms are currently using simple entities called test cases that are concretely realised as executable scripts. Our key idea is to provide test cases with more reasoning, adaptive behaviour and learning capabilities by using the concepts of intelligent software agents. We refer to such test cases as test agents. The model that underlie a test agent is capable of flexible and autonomous actions in order to meet overall testing objectives. Our goal is to increase the decentralisation of regression testing by letting test agents to know for themselves when they should be executing, how they should update their purpose, and when they should interact with each other. In this paper, we envision software test agents that display such adaptive autonomous behaviour. Emerging developments and challenges regarding the use of test agents are explored-in particular, new research that seeks to use adaptive autonomous agents in software testing.

Growth of software size, lack of resources to perform regression testing, and failure to detect bugs faster have seen increased reliance on continuous integration and test automation. Even with greater hardware and software resources dedicated to test automation, software testing is faced with enormous challenges, resulting in increased dependence on centralized and complex mechanisms for automated test case selection as part of continuous integration. These mechanisms are currently using static entities called test cases that are concretely realized as executable scripts. Our key vision is to provide test cases with more reasoning, adaptive behavior and learning capabilities by using the concepts of software agents. We refer to such test cases as test agents. The model that underlie a test agent is capable of flexible and autonomous actions in order to meet overall testing objectives. Our goal is to increase the decentralization of regression testing by letting test agents to know for themselves when they should be executing, how they should update their purpose, and when they should interact with each other. In this paper, we envision test agents that display such adaptive autonomous behavior. Existing and emerging developments and challenges regarding the use of test agents are explored - in particular, new research that seeks to use adaptive autonomous agents in software testing. 

Software testing is a difficult, intellectual activity performed in a social environment. Naturally, testers use and allocate multiple cognitive resources towards this task. The goal of this study is to understand better the routine and behaviour of human testers and their mental models when performing testing. We investigate this topic by surveying 38 software testers and developers in Sweden. The survey explores testers’ cognitive processes when performing testing by investigating the knowledge they bring, the activities they select and perform, and the challenges they face in their routine. By analyzing the survey results, we provide a characterization of tester practices and identify insights regarding the problem-solving process. We use these descriptions to further enhance a cognitive model of software testing. © 2023, IFIP International Federation for Information Processing.

In this paper we present a modeling and analysis tool for service-oriented systems. The tool enables graphical modeling of service-based systems, within the resource-aware timed behavioral language Remes, as well as a textual system description. We have developed a graphical environment where services can be composed as desired by the user, together with a textual service composition interface in which compositions can also be checked for correctness. We also provide automated traceability between the two design interfaces, which results in a tool that enhances the potential of system design by intuitive service manipulation. The paper presents the design principles, infrastructure, and the user interface of our tool.

A system’s architecture influence on the functions and other properties of embedded systems makes its high level analysis and verification very desirable. EAST-ADL is an architecture description language dedicated to automotive embedded system design with focus on structural and functional modeling. The behavioral description is not integrated within the execution semantics, which makes it harder to transform, analyze, and verify EAST-ADL models. Model-based techniques help address this issue by enabling automated transformation between different design models, and providing means for simulation and verification. We present a verification tool, called ViTAL, which provides the possibility to express the functional EAST-ADL behavior as timed automata models, which have precise semantics and can be formally verified. The ViTAL tool enables the transformation of EAST-ADL functional models to the UPPAAL PORT tool for model checking. This method improves the verification of functional and timing requirements in EAST-ADL, and makes it possible to identify dependencies and potential conflicts between different vehicle functions before the actual AUTOSAR implementation.

A system’s architecture influence on the functions and other properties of embedded systems makes its high level analysis and verification very desirable. EAST-ADL is an architecture description language dedicated to automotive embedded system design with focus on structural and functional modeling. The behavioral description is not integrated within the execution semantics, which makes it harder to transform, analyze, and verify EAST-ADL models. Model-based techniques help address this issue by enabling automated transformation between different design models, and providing means for simulation and verification. We present a verification tool, called ViTAL, which provides the possibility to express the functional EAST-ADL behavior as timed automata models, which have precise semantics and can be formally verified. The ViTAL tool enables the transformation of EAST-ADL functional models to the UPPAAL PORT tool for model checking. This method improves the verification of functional and timing requirements in EAST-ADL, and makes it possible to identify dependencies and potential conflicts between different vehicle functions before the actual AUTOSAR implementation.

Nowadays, embedded systems are increasingly complex, meaning that traditional testing methods are costly to use and infeasible to directly apply due to the complex interactions between hardware and software. Modern embedded systems are also demanded to function based on low-energy computing. Hence, testing the energy usage is increasingly important. Artifacts produced during the development of embedded systems, such as architectural descriptions, are beneficial abstractions of the system’s complex structure and behavior. Electronic Architecture and Software Tools Architecture Description Language (EAST-ADL) is one such example of a domain-specific architectural language targeting the automotive industry. In this paper, we propose a method for testing design models using EAST-ADL architecture mutations. We show how fault-based testing can be used to generate, execute and select tests using energy-aware mutants-- syntactic changes in the architectural description, used to mimic naturally occurring energy faults. Our goal is to improve testing of complex embedded systems by moving the testing bulk from the actual systems to models of their behaviors and non-functional requirements. We combine statistical model-checking, increasingly used in quality assurance of embedded systems, with EAST-ADL architectural models and mutation testing to drive the search for faults. We show the results of applying this method on an industrial-sized system developed by Volvo GTT. The results indicate that model testing of EAST-ADL architectural models can reduce testing complexity by bringing early and cost-effective automation.

Testing is an important activity in engineering of industrial embedded software. In certain application domains (e.g., railway industry) engineering software is certified according to safety standards that require extensive software testing procedures to be applied for the development of reliable systems. Mutation analysis is a technique for creating faulty versions of a software for the purpose of examining the fault detection ability of a test suite. Mutation analysis has been used for evaluating existing test suites, but also for generating test suites that detect injected faults (i.e., mutation testing). To support developers in software testing, we propose a technique for producing test cases using an automated test generation approach that operates using mutation testing for software written in IEC 61131-3 language, a programming standard for safety-critical embedded software, commonly used for Programmable Logic Controllers (PLCs). This approach uses the Uppaal model checker and is based on a combined model that contains all the mutants and the original program. We applied this approach in a tool for testing industrial PLC programs and evaluated it in terms of cost and fault detection. For realistic validation we collected industrial experimental evidence on how mutation testing compares with manual testing as well as automated decision-coverage adequate test generation. In the evaluation, we used manually seeded faults provided by four industrial engineers. The results show that even if mutation-based test generation achieves better fault detection than automated decision coverage-based test generation, these mutation-adequate test suites are not better at detecting faults than manual test suites. However, the mutation-based test suites are significantly less costly to create, in terms of testing time, than manually created test suites. Our results suggest that the fault detection scores could be improved by considering some new and improved mutation operators (e.g., Feedback Loop Insertion Operator (FIO)) for PLC programs as well as higher-order mutations.

Automated test generation has been suggested as a way of creating tests at a lower cost. Nonetheless, it is not very well studied how such tests compare to manually written ones in terms of cost and effectiveness. This is particularly true for industrial control software, where strict requirements on both specification-based testing and code coverage typically are met with rigorous manual testing. To address this issue, we conducted a case study in which we compared manually and automatically created tests. We used recently developed real-world industrial programs written in the IEC 61131-3, a popular programming language for developing industrial control systems using programmable logic controllers. The results show that automatically generated tests achieve similar code coverage as manually created tests, but in a fraction of the time (an average improvement of roughly 90%). We also found that the use of an automated test generation tool does not result in better fault detection in terms of mutation score compared to manual testing. Specifically, manual tests more effectively detect logical, timer and negation type of faults, compared to automatically generated tests. The results underscore the need to further study how manual testing is performed in industrial practice and the extent to which automated test generation can be used in the development of reliable systems.

Testing is an important activity in engineering of industrial embedded software. In certain application domains (e.g., railway industry) engineering software is certified according to safety standards that require extensive software testing procedures to be applied for the development of reliable systems. Mutation analysis is a technique for creating faulty versions of a software for the purpose of examining the fault detection ability of a test suite. Mutation analysis has been used for evaluating existing test suites, but also for generating test suites that detect injected faults (i.e., mutation testing). To support developers in software testing, we propose a technique for producing test cases using an automated test generation approach that operates using mutation testing for software written in IEC 61131-3 language, a programming standard for safety-critical embedded software, commonly used for Programmable Logic Controllers (PLCs). This approach uses the Uppaal model checker and is based on a combined model that contains all the mutants and the original program. We applied this approach in a tool for testing industrial PLC programs and evaluated it in terms of cost and fault detection. For realistic validation we collected industrial experimental evidence on how mutation testing compares with manual testing as well as automated decision-coverage adequate test generation. In the evaluation, we used manually seeded faults provided by four industrial engineers. The results show that even if mutation-based test generation achieves better fault detection than automated decision coverage-based test generation, these mutation-adequate test suites are not better at detecting faults than manual test suites. However, the mutation-based test suites are significantly less costly to create, in terms of testing time, than manually created test suites. Our results suggest that the fault detection scores could be improved by considering some new and improved mutation operators (e.g., Feedback Loop Insertion Operator (FIO)) for PLC programs as well as higher-order mutations.

A method for model-based test generation of safety-critical embedded applications using Programmable Logic Controllers and implemented in a programming language such as Function Block Diagram (FBD) is described. The FBD component model is based on the IEC 1131 standard and it is used primarily for embedded systems, in which timeliness is an important property to be tested. Our method involves the transformation of FBD programs with timed annotations into timed automata models which are used to automatically generate test suites. Specifically we demonstrate how to use model transformation for formalization and model checking of FBD programs using the UPPAAL tool. Many benefits emerge from this method, including the ability to automatically generate test suites from a formal model in order to ensure compliance to strict quality requirements including unit testing and specific coverage measurements. The approach is experimentally assessed on a train control system in terms of consumed resources.

In model-driven development, testers are often focusing on functional model-level testing, enabling verification of design models against their specifications. In addition, in safety-critical software development, testers are required to show that tests cover the structure of the implementation. Testing cost and time savings could be achieved if the process of deriving test cases for logic coverage is automated and provided test cases are ready to be executed. The logic coverage artifacts, i.e., predicates and clauses, are required for different logic coverage, e.g., MC/DC. One way of dealing with test case generation for ensuring logic coverage is to approach it as a model-checking problem, such that model-checking tools automatically create test cases. We show how logic coverage criteria can be formalized and used by a model-checker to provide test cases for ensuring this coverage on safety-critical software described in the Function Block Diagram programming language. Based on our experiments, this approach, supported by a tool chain, is an applicable and useful way of generating test cases for covering Function Block Diagrams.

VeriDevOps offers a methodology and a set of integrated mechanisms that significantly improve automation in DevOps to protect systems at operations time and prevent security issues at development time by (1) specifying security requirements, (2) generating trace monitors, (3) locating root causes of vulnerabilities, and (4) identifying security flaws in code and designs. This paper presents a methodology that enhances productivity and enables the continuous integration/delivery of trustworthy systems. We outline the methodology, its application to relevant scenarios, and offer recommendations for engineers and managers adopting the VeriDevOps approach. Practitioners applying the VeriDevOps methodology should include security modeling in the DevOps process, integrate security verification throughout all stages, utilize automated test generation tools for security requirements, and implement a comprehensive security monitoring system, with regular review and update procedures to maintain relevance and effectiveness.

Software testing is a complex, intellectual activity based (at least) on analysis, reasoning, decision making, abstraction and collaboration performed in a highly demanding environment. Naturally, it uses and allocates multiple cognitive resources in software testers. However, while a cognitive psychology perspective is increasingly used in the general software engineering literature, it has yet to find its place in software testing. To the best of our knowledge, no theory of software testers’ cognitive processes exists. Here, we take a first step towards such a theory by presenting a cognitive model of software testing based on how problem solving is conceptualized in cognitive psychology. Our approach is to instantiate a general problem solving process for the specific problem of creating test cases. We then propose a controlled experiment for testing our cognitive test design model. The experiment makes use of verbal protocol analysis to understand the mechanisms by which human testers choose, design, implement and evaluate test cases and test design strategies. An initial evaluation was then performed with five students as subjects. The results support a problem solving-based model of test design for capturing testers’ cognitive processes.

Combinatorial testing has been suggested as an effective method of creating test cases at a lower cost. However, industrially applicable tools for modeling and combinatorial test generation are still scarce. As a direct effect, combinatorial testing has only seen a limited uptake in industry that calls into question its practical usefulness. This lack of evidence is especially troublesome if we consider the use of combinatorial test generation for industrial safety-critical control software, such as are found in trains, airplanes, and power plants. To study the industrial application of combinatorial testing, we evaluated ACTS, a popular tool for combinatorial modeling and test generation, in terms of applicability and test efficiency on industrial-sized IEC 61131-3 industrial control software running on Programmable Logic Controllers (PLC). We assessed ACTS in terms of its direct applicability in combinatorial modeling of IEC 61131-3 industrial software and the efficiency of ACTS in terms of generation time and test suite size. We used 17 industrial control programs provided by Bombardier Transportation Sweden AB and used in a train control management system. Our results show that not all combinations of algorithms and interaction strengths could generate a test suite within a realistic cut-off time. The results of the modeling process and the efficiency evaluation of ACTS are useful for practitioners considering to use combinatorial testing for industrial control software as well as for researchers trying to improve the use of such combinatorial testing techniques.

System testing is essential for developing high-quality systems, but the degree of automation in system testing is still low. Therefore, there is high potential for Artificial Intelligence (AI) techniques like machine learning, natural language processing, or search-based optimization to improve the effectiveness and efficiency of system testing. This chapter presents where and how AI techniques can be applied to automate and optimize system testing activities. First, we identified different system testing activities (i.e., test planning and analysis, test design, test execution, and test evaluation) and indicated how AI techniques could be applied to automate and optimize these activities. Furthermore, we presented an industrial case study on test case analysis, where AI techniques are applied to encode and group natural language into clusters of similar test cases for cluster-based test optimization. Finally, we discuss the levels of autonomy of AI in system testing. 

Combinatorial coverage has been proposed as a way to measure the quality of test cases by using the input interaction characteristics. This paper describes the results of empirically measuring combinatorial coverage of manually created test cases by experienced industrial engineers. We found that manual test cases achieve on average 78% 2-way combinatorial coverage, 57% 3-way coverage, 40% 4-way coverage, 20% 5-way combinatorial coverage and 13% for 6-way combinatorial coverage. These manual test cases can be augmented to achieve 100% combinatorial coverage for 2-way and 3-way interactions by adding eight and 66 missing tests on average, respectively. For 4-way interactions, full combinatorial coverage can achieved by adding 658 missing tests. For 5-way and 6-way interactions, full combinatorial coverage can be achieved by adding 5163 and 6170 missing tests on average, respectively. The results of this paper suggest that manual tests created by industrial engineers do no achieve high combinatorial coverage and can be improved by using combinatorial testing at the expense of the number of test cases to be executed.

Test cases that only observe the system under test can improve parallelism and detection of faults occurring due to unanticipated feature interactions. Traditionally, such \textit{passive} test cases have been challenging to express, partly due to the use of complex mathematical notations. The T-EARS (Timed Easy Approach to Requirements Syntax) language prototype was introduced to respond to this and has received positive feedback from practitioners. However, the prototype suffered from few deficiencies, such as allowing non-intuitive combinations of expressions and usage of temporal specifiers that quickly got difficult to understand. This paper builds on the T-EARS prototype and input from experienced testers on a previous iteration of the language. The collected experience was applied to a new prototype using a structured update process, including a set of system-level requirements from a vehicular software system. The results include a new, improved grammar for the T-EARS language and a description of the evaluation semantics.

Test cases that only observe the system under test can improve parallelism and detection of faults occurring due to unanticipated feature interactions. Traditionally, such passive test cases have been challenging to express, partly due to the use of complex mathematical notations. The T-EARS (Timed Easy Approach to Requirements Syntax) language prototype was introduced to respond to this and has received positive feedback from practitioners. However, the prototype suffered from few deficiencies, such as allowing non-intuitive combinations of expressions and usage of temporal specifiers that quickly got difficult to understand. This paper builds on the T-EARS prototype and input from experienced testers on a previous iteration of the language. The collected experience was applied to a new prototype using a structured update process, including a set of system-level requirements from a vehicular software system. The results include a new, improved grammar for the T-EARS language and a description of the evaluation semantics.

In large-scale embedded system development, requirements are often expressed in natural language. Translating these requirements to executable test cases, while keeping the test cases and requirements aligned, is a challenging task. While such a transformation typically requires extensive domain knowledge, we show that a systematic process in combination with passive testing would facilitate the translation as well as linking the requirements to tests. Passive testing approaches observe the behavior of the system and test their correctness without interfering with the normal behavior. We use a specific approach to passive testing: guarded assertions (G/A). This paper presents a method for transforming system requirements expressed in natural language into G/As. We further present a proof of concept evaluation, performed at Bombardier Transportation Sweden AB, in which we show how the process would be used, together with practical advice of the reasoning behind the translation steps.

Passive testing continuously observes the system or system execution logs without any interference or instrumentation to test diverse combinations of functions, resulting in a more thorough evaluation over time. However, reaching a working solution to passive testing is not without challenges. While there have been some efforts to extract information from system requirements to create passive test cases, to our knowledge, no such efforts are mature enough to be applied in a real, industrial safety-critical context. Our passive testing approach uses the Timed - Easy Approach to Requirements Syntax (T-EARS) specification language and its accompanying tool-chain. This study reports challenges and solutions to introducing system-level passive testing for a vehicular safety-critical system through industrial data analysis, including 116 safety-related requirements. Our results show that passive testing using the T-EARS language and its tool-chain can be used for system-level testing in an industrial setting for 64% of the studied requirements. We identified several sources of false positive results and show how to tune test cases to reduce such false positives systematically. Finally, we show the requirement coverage achieved by a manual test session and that passive testing using T-EARS can find a set of injected faults that are considered hard to find with other test techniques.

Mission planning for multi-agent autonomous systems aims to generate feasible and optimal mission plans that satisfy the given requirements. In this article, we propose a mission-planning methodology that combines (i) a path-planning algorithm for synthesizing path plans that are safe in environments with complex road conditions, and (ii) a task-scheduling method for synthesizing task plans that schedule the tasks in the right and fastest order, taking into account the planned paths. The task-scheduling method is based on model checking, which provides means of automatically generating task execution orders that satisfy the requirements and ensure the correctness and efficiency of the plans by construction. We implement our approach in a tool named MALTA, which offers a user-friendly GUI for configuring mission requirements,  a module for path planning, an integration with the model checker UPPAAL, and functions for automatic generation of formal models, and parsing of the execution traces of models. Experiments with the tool demonstrate its applicability and performance in various configurations of an industrial case study of an autonomous quarry. We also show the adaptability of our tool by employing it on a special case of the industrial case study.