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Text mining techniques, particularly those leveraging machine learning for natural language processing, have gained significant attention for qualitative data analysis in software testing. However, their complexity and lack of transparency can pose challenges, especially in safety-critical domains where simpler, interpretable solutions are often preferred unless accuracy is heavily compromised. This study investigates the trade-offs between complexity, effort, accuracy, and utility in text mining and clustering techniques, focusing on their application for detecting functional dependencies among manual integration test cases in safety-critical systems. Using empirical data from an industrial testing project at ALSTOM Sweden, we evaluate various string distance methods, NCD compressors, and machine learning approaches. The results highlight the impact of preprocessing techniques, such as tokenization, and intrinsic factors, such as text length, on algorithm performance. Findings demonstrate how text mining and clustering can be optimized for safety-critical contexts, offering actionable insights for researchers and practitioners aiming to balance simplicity and effectiveness in their testing workflows.

The revised 2024 Energy Performance of Buildings Directive (EPBD) has strengthened the use of the EnergyPerformance Certificate (EPC) as the key tool for assessing and classifying the energy efficiency of buildings. Heterogeneity persists, however, in how ratings are interpreted, implemented and made accessible, within and across EU countries. Significant challenges still exist in terms of cross-country alignment, with capabilities of AI and ML for global region analysis underexploited, and research overlaps common. This paper investigates ways in which the Colouring Cities Research Programme (CCRP), and its network of research-led, open spatial data visualisation platforms, can support wider and more consistent EPC implementation, and public dialogue around energy targets. Drawing on case studies from Sweden and Britain, it compares methods for generating energy ratings; EPC openness and accessibility; and highlights the benefits of publicly visualising EPCs at multiple scales using a shared data schema. Results show the value of interactive and integrated open platform networks, for mapping and contextualising EPC data, increasing crosssector engagement, and helping nudge behaviour to accelerate decarbonisation in the building sector.

To combine the relative strengths of fully preemptive and non-preemptive fixed priority scheduling, we can use limited preemptive scheduling methods. One such method is fixed-priority threshold scheduling (FPTS). This approach defines dual priorities for every task, a priority assigned to the inactive jobs and an equal or a higher one, called preemption threshold, for the jobs that have already started executing. A task can preempt another task's job if and only if its priority is greater than the other task's preemption threshold. FPTS can be implemented by assigning tasks to non-preemptive groups in a one-to-many manner. That is groups of tasks that are mutually non-preemptive.

Cloud computing is a game-changer model that opens new directions for modern manufacturing. It enables services and solutions that help improve the productivity and efficiency of smart production plants. The main objective of the paper is to provide a summary of the various cloud-based manufacturing services currently being offered to manufacturers or that could be offered in the future. Additionally, the paper aims to discuss the various enabling technologies used to support the integration of cloud manufacturing in the manufacturing industry. Furthermore, the paper categorizes the different services based on their functionalities and maps them to four levels of production such as plant level, production line level, machine level, and process level. The categorization of services and mapping them to appropriate levels in production can enhance efficiency and productivity in the manufacturing industry. The study advances the discussion on cloud-based manufacturing from the types of services and enabling technologies perspective.

The study examines the remote integration process of advanced manufacturing technology (AMT) into the production system and identifies key challenges and mitigating actions for a smoother introduction and integration process.

Design/methodology/approach: The study adopts a case study approach to a cyber-physical production system at an industrial technology center using a mobile robot as an AMT.

Findings: By applying the plug-and-produce concept, the study exemplifies an AMT's remote integration process into a cyber-physical production system in nine steps. Eleven key challenges and twelve mitigation actions for remote integration are described based on technology–organization–environment theory. Finally, a remote integration framework is proposed to facilitate AMT integration into production systems.

Practical implications: The study presents results purely from a practical perspective, which could reduce dilemmas in early decision-making related to smart production. The proposed framework can improve flexibility and decrease the time needed to configure new AMTs in existing production systems.

Originality/value: The area of remote integration for AMT has not been addressed in depth before. The consequences of lacking in-depth studies for remote integration imply that current implementation processes do not match the needs and the existing situation in the industry and often underestimate the complexity of considering both technological and organizational issues. The new integrated framework can already be deployed by industry professionals in their efforts to integrate new technologies with shorter time to volume and increased quality but also as a means for training employees in critical competencies required for remote integration.

Artificial Intelligence Methods for Optimization of the Software Testing Process: With Practical Examples and Exercises presents different AI-based solutions for overcoming the uncertainty found in many initial testing problems. The concept of intelligent decision making is presented as a multi-criteria, multi-objective undertaking. The book provides guidelines on how to manage diverse types of uncertainty with intelligent decision-making that can help subject matter experts in many industries improve various processes in a more efficient way. As the number of required test cases for testing a product can be large (in industry more than 10,000 test cases are usually created). Executing all these test cases without any particular order can impact the results of the test execution, hence this book fills the need for a comprehensive resource on the topics on the how's, what's and whys. To learn more about Elsevier’s Series, Uncertainty, Computational Techniques and Decision Intelligence, please visit this link: https://www.elsevier.com/books-and-journals/book-series/uncertainty-computational-techniques-and-decision-intelligence 

The aims of this research are to enhance industry-university collaboration and to design learning experiences connecting the research front to practitioners. We present an empirical study with a qualitative approach involving teachers who gathered data from newly developed advanced level courses in artificial intelligence, energy, environmental, and systems engineering. The study is part of FutureE, an academic development project over 3 years involving 12 courses. The project, as well as this study, is part of a cross-disciplinary collaboration effort. Empirical data comes from course evaluations, course analysis, teacher workshops, and semi-structured interviews with selected students, who are also professionals. This paper will discuss course design and course implementation by presenting dilemmas and paradoxes. Flexibility is key for the completion of studies while working. Academia needs to develop new ways to offer flexible education for students from a professional context, but still fulfil high quality standards and regulations as an academic institution. Student-to-student interactions are often suggested as necessary for qualified learning, and students support this idea but will often not commit to it during courses. Other dilemmas are micro-sized learning versus vast knowledge, flexibility versus deadlines as motivating factors, and feedback hunger versus hesitation to share work. Furthermore, we present the challenges of providing equivalent online experience to practical in-person labs. On a structural level, dilemmas appear in the communication between university management and teachers. These dilemmas are often the result of a culture designed for traditional campus education. We suggest a user-oriented approach to solve these dilemmas, which involves changes in teacher roles, culture, and processes. The findings will be relevant for teachers designing and running courses aiming to attract professionals. They will also be relevant for university management, building a strategy for lifelong e-learning based on co-creation with industry.

Detecting the dependency between integration test cases plays a vital role in the area of software test optimization. Classifying test cases into two main classes - dependent and independent - can be employed for several test optimization purposes such as parallel test execution, test automation, test case selection and prioritization, and test suite reduction. This task can be seen as an imbalanced classification problem due to the test cases' distribution. Often the number of dependent and independent test cases is uneven, which is related to the testing level, testing environment and complexity of the system under test. In this study, we propose a novel methodology that consists of two main steps. Firstly, by using natural language processing we analyze the test cases' specifications and turn them into a numeric vector. Secondly, by using the obtained data vectors, we classify each test case into a dependent or an independent class. We carry out a supervised learning approach using different methods for handling imbalanced datasets. The feasibility and possible generalization of the proposed methodology is evaluated in two industrial projects at Bombardier Transportation, Sweden, which indicates promising results.

Software testing is still heavily dependent on human judgment since a large portion of testing artifacts such as requirements and test cases are written in a natural text by people. Identifying and classifying relevant test cases in large test suites is a challenging and also time-consuming task. Moreover, to optimize the testing process test cases should be distinguished based on their properties such as their dependencies and similarities. Knowing the mentioned properties at an early stage of the testing process can be utilized for several test optimization purposes such as test case selection, prioritization, scheduling, and also parallel test execution. In this paper, we apply, evaluate, and compare the performance of two deep learning algorithms to detect the similarities between manual integration test cases. The feasibility of the mentioned algorithms is later examined in a Telecom domain by analyzing the test specifications of five different products in the product development unit at Ericsson AB in Sweden. The empirical evaluation indicates that utilizing deep learning algorithms for finding the similarities between manual integration test cases can lead to outstanding results.

Knowing about dependencies and similarities between test cases is beneficial for prioritizing them for cost-effective test execution. This holds especially true for the time consuming, manual execution of integration test cases written in natural language. Test case dependencies are typically derived from requirements and design artifacts. However, such artifacts are not always available, and the derivation process can be very time-consuming. In this paper, we propose, apply and evaluate a novel approach that derives test cases' similarities and functional dependencies directly from the test specification documents written in natural language, without requiring any other data source. Our approach uses an implementation of Doc2Vec algorithm to detect text-semantic similarities between test cases and then groups them using two clustering algorithms HDBSCAN and FCM. The correlation between test case text-semantic similarities and their functional dependencies is evaluated in the context of an on-board train control system from Bombardier Transportation AB in Sweden. For this system, the dependencies between the test cases were previously derived and are compared to the results our approach. The results show that of the two evaluated clustering algorithms, HDBSCAN has better performance than FCM or a dummy classifier. The classification methods' results are of reasonable quality and especially useful from an industrial point of view. Finally, performing a random undersampling approach to correct the imbalanced data distribution results in an F1 Score of up to 75% when applying the HDBSCAN clustering algorithm.