Maria Laura Brzezinski Meyer

Maria Laura Brzezinski Meyer

PhD in Automation and Computing Science

Publications

A Case Study on the ”Jungle” Search for Industry-Relevant Regression Testing

Published in 2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security (QRS), 2022

The optimization of regression testing (RT) has been widely studied in the literature, and numerous methods exist. However, each context is unique. Therefore, how to tell which method is appropriate for a specific industrial context? Recent work has proposed a taxonomy to aid in answering this question. The approach is to map both the RT problem and existing solutions onto the taxonomy, aiming to determine which solutions are best aligned with the problem. This paper presents a case study that evaluates the approach in a real setting. The context is the development of R&D projects at a major automotive company, in the domain of connected vehicles. We used the taxonomy to characterize the RT problem in terms of measurable effects, and to identify the technically feasible solutions from a set of 52 papers. We report on the beneficial aspects but also the difficulties of the approach, due to unclear taxonomy elements, missing ones and paper classification errors.

Recommended citation: M. L. Brzezinski Meyer, H. Waeselynck and F. Cuesta, “A Case Study on the “Jungle” Search for Industry-Relevant Regression Testing,” 2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security (QRS), Chiang Mai, Thailand, 2023, pp. 382-393, doi: 10.1109/QRS60937.2023.00045. https://ieeexplore.ieee.org/document/10366626

TSAI - Test Selection using Artificial Intelligence for the Support of Continuous Integration

Published in 2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), 2022

The agile methodology has been increasingly deployed in the industry world, breaking the process into cycles of planning, executing, and evaluating. In the software development domain, an agile method named continuous integration is widely used to automatically integrate code changes from different developers into the same software. Then, each new build can be tested to make sure that the modifications did not interfere with the rest of the already verified code. Despite being very important, regression tests are usually the costliest part of a project. It is laborious to retest all tests of each new software version due to the time it takes to perform and often, before all tests are finished, a new software version is ready to be tested. To improve regression tests results, a selection can be done. By selecting the right tests at the right moment, the use of all test catalogs can be avoided to find faults in the software tested. The aim of this work is to develop a method to select tests to be executed for each version using artificial intelligence algorithms. Learning algorithms can find patterns and similarities between test cases to help knowing which one has a higher probability to expose a fault.

Recommended citation: M. L. Brzezinski Meyer, "TSAI - Test Selection using Artificial Intelligence for the Support of Continuous Integration," 2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), Wuhan, China, 2021, pp. 306-309, doi: 10.1109/ISSREW53611.2021.00092. https://ieeexplore.ieee.org/document/9700356

Combining Machine Learning and Behavior Analysis Techniques for Network Security

Published in 2020 International Conference on Information Networking (ICOIN), 2020

Network traffic attacks are increasingly common and varied, this is a big problem especially when the target network is centralized. The creation of IDS (Intrusion Detection Systems) capable of detecting various types of attacks is necessary. Machine learning algorithms are widely used in the classification of data, bringing a good result in the area of computer networks. In addition, the analysis of entropy and distance between data sets are also very effective in detecting anomalies. However, each technique has its limitations, so this work aims to study their combination in order to improve their performance and create a new intrusion detection system capable of well detect some of the most common attacks. Reliability indices will be used as metrics to the combination decision and they will be updated in each new dataset according to the decision made earlier.

Recommended citation: M. L. Brzezinski Meyer and Y. Labit, "Combining Machine Learning and Behavior Analysis Techniques for Network Security," 2020 International Conference on Information Networking (ICOIN), Barcelona, Spain, 2020, pp. 580-583, doi: 10.1109/ICOIN48656.2020.9016500. https://ieeexplore.ieee.org/document/9016500