https://www.mdu.se/

To ensure the proper testing of any software product, it is imperative to cover various functional and nonfunctional requirements at different testing levels (e.g., unit or integration testing). Ensuring appropriate testing requires making a series of decisions-e.g., assigning features to distinct Continuous Integration (CI) configurations or determining which test specifications to automate. Such decisions are generally made manually and require in-depth domain knowledge. This study introduces, implements, and evaluates ITMOS (Intelligent Test Management Optimization System), an intelligent test management system designed to optimize decision-making during the software testing process. ITMOS efficiently processes new requirements presented in natural language, segregating each requirement into appropriate CI configurations based on predefined quality criteria. Additionally, ITMOS has the capability to suggest a set of test specifications for test automation. The feasibility and potential applicability of the proposed solution were empirically evaluated in an industrial telecommunications project at Ericsson. In this context, ITMOS achieved accurate results for decision-making tasks, exceeding the requirements set by domain experts.

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.

In modern 5 G radio manufacturing, traditional quality control methods based on black fixed thresholds are increasingly inadequate, often failing to capture nuanced fault patterns and requiring substantial manual intervention. This study presents DTA-QC, an AI-driven framework for adaptive thresholding and intelligent test optimization in 5 G production environments. The proposed system introduces three core innovations: (1) dynamic thresholding using LSTM autoencoders and regression models to detect anomalies under evolving production conditions, (2) supervised fault classification via convolutional neural networks trained on time-windowed sensor data, and (3) a four-level severity classification system (Normal, Warning, Worse, Stop) to support real-time decision-making in manufacturing environments. DTA-QC is implemented and validated on Ericsson AB's 5 G radio production line, achieving high anomaly detection accuracy (ROC-AUC: 0.89-0.94) and significantly reducing manual review efforts, without requiring specialized hardware. To assess generalizability, DTA-QC is further evaluated on a public benchmark dataset. A comparative analysis of three architectural variants revealed trade-offs in complexity, latency, and deployment feasibility. These results underscore the value of embedding AI-driven analytics in industrial test workflows, contributing to the broader goals of intelligent manufacturing and adaptive, data-driven quality assurance.

The relationship between requirements engineering and testing has been a key interest throughout my research career. It cannot be that requirements engineers are from Omicron Persei 7 and testers from Omicron Persei 9we all live on the same planet. Im happy to co-author this column with a former collaborator from a test automation EU project, now a manager at Ericsson. With this issue's focus on AI-powered testing, we ask: How can we define corresponding tool and process requirements in practice? This column is twofold. First, Sahar shares her experience. Then, we connect her observations to findings from a longitudinal study on tool adoption. Together, the perspectives give a grounded view from the field-arkus Borg.

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. 

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 

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.

Finding a balance between testing goals and testing resources can be considered as a most challenging issue, therefore test optimization plays a vital role in the area of software testing. Several parameters such as the objectives of the tests, test cases similarities and dependencies between test cases need to be considered, before attempting any optimization approach. However, analyzing corresponding testing artifacts (e.g. requirement specification, test cases) for capturing the mentioned parameters is a complicated task especially in a manual testing procedure, where the test cases are documented as a natural text written by a human. Thus, utilizing artificial intelligence techniques in the process of analyzing complex and sometimes ambiguous test data, is considered to be working in different industries. Test scheduling is one of the most popular and practical ways to optimize the testing process. Having a group of test cases which are required the same system setup, installation or testing the same functionality can lead to a more efficient testing process. In this paper, we propose, apply and evaluate a natural language processing-based approach that derives test cases' similarities directly from their test specification. The proposed approach utilizes the Levenshtein distance and converts each test case into a string. Test cases are then grouped into several clusters based on their similarities. Finally, a set of cluster-based parallel test scheduling strategies are proposed for execution. The feasibility of the proposed approach is studied by an empirical evaluation that has been performed on a Telecom use-case at Ericsson in Sweden and indicates promising results.

Highly accurate weather classifiers have recently received a great deal of attention due to their promising applications. An alternative to conventional weather radars consists of using the measured attenuation data in commercial microwave links (CML) as input to a weather classifier. The design of an accurate weather classifier is challenging due to diverse weather conditions, the absence of predefined features, and specific domain requirements in terms of execution time and detection sensitivity. In addition to this, the quality of the data given as input to the classifier plays a crucial role as it directly impacts the classification output. However, the quality of the measured attenuation data in the CMLs poses a serious concern for different reasons, e.g. the nature of the data itself, the location of each link, and the geographical distance between the links. This mandates the adoption of a data preprocessing step before classification with the purpose to validate the quality of the input data. In this paper, we propose a data preprocessing framework which employs a deep learning model to (i) detect anomalies in the raw data and (ii) validate the measured CML attenuation data by adding quality flags. Moreover, the feasibility and possible generalizations of the proposed framework are studied by conducting an empirical case study performed on real data collected from CMLs at a large Telecom company.

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.