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7th International Workshop on Automotive System/Software Architecture (WASA 2021)
- 2021
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Editorial proceedings (other academic/artistic)abstract
- This volume contains the papers presented at the 7th International Workshop on Automotive System/Software Architecture (WASA 2021) held on March 22, 2021, in Stuttgart, Germany. WASA was organized as part of the 18th IEEE International Conference on Software Architecture (ICSA 2021), the premier software architecture conference. Due to the worldwide SARS-CoV-2 pandemic, the main conference and the workshop were hosted virtually.
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- Abrahao, Silvia, et al.
(author)
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Modeling and Architecting of Complex Software Systems
- 2024
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In: IEEE SOFTWARE. - 0740-7459 .- 1937-4194. ; 41:3, s. 76-79
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Journal article (peer-reviewed)abstract
- This edition of the "Practitioners' Digest" covers recent papers on novel approaches and tools to assist developers in modeling and architecting software systems from two conferences: the 26th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS) and the 20th IEEE International Conference on Software Architecture (ICSA). Feedback or suggestions are welcome. Also, if you try or adopt any of the practices included in the column, please send us and the authors of the paper(s) a note about your experiences.
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- Abrahao, Silvia, et al.
(author)
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Open Source Software: Communities and Quality
- 2023
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In: IEEE Software. - 1937-4194 .- 0740-7459. ; 40:4, s. 96-99
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Journal article (peer-reviewed)abstract
- This edition of the Practitioner's Digest features recent papers on open source software related to toxicity in open source discussions, newcomers in open source projects, quality of ansible scripts, code review practices, orphan vulnerabilities in open source software, and the relationship between community and design smells.
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- Al Sabbagh, Khaled, 1987, et al.
(author)
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A classification of code changes and test types dependencies for improving machine learning based test selection
- 2021
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In: SIGPLAN Notices (ACM Special Interest Group on Programming Languages). - New York, NY, USA : ACM. - 0730-8566. ; , s. 40-49
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Conference paper (peer-reviewed)abstract
- Machine learning has been increasingly used to solve various software engineering tasks. One example of their usage is in regression testing, where a classifier is built using historical code commits to predict which test cases require execution. In this paper, we address the problem of how to link specific code commits to test types to improve the predictive performance of learning models in improving regression testing. We design a dependency taxonomy of the content of committed code and the type of a test case. The taxonomy focuses on two types of code commits: changing memory management and algorithm complexity. We reviewed the literature, surveyed experienced testers from three Swedish-based software companies, and conducted a workshop to develop the taxonomy. The derived taxonomy shows that memory management code should be tested with tests related to performance, load, soak, stress, volume, and capacity; the complexity changes should be tested with the same dedicated tests and maintainability tests. We conclude that this taxonomy can improve the effectiveness of building learning models for regression testing.
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- Al Sabbagh, Khaled, 1987, et al.
(author)
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Improving Data Quality for Regression Test Selection by Reducing Annotation Noise
- 2020
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In: Proceedings - 46th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2020. ; , s. 191-194
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Conference paper (peer-reviewed)abstract
- Big data and machine learning models have been increasingly used to support software engineering processes and practices. One example is the use of machine learning models to improve test case selection in continuous integration. However, one of the challenges in building such models is the identification and reduction of noise that often comes in large data. In this paper, we present a noise reduction approach that deals with the problem of contradictory training entries. We empirically evaluate the effectiveness of the approach in the context of selective regression testing. For this purpose, we use a curated training set as input to a tree-based machine learning ensemble and compare the classification precision, recall, and f-score against a non-curated set. Our study shows that using the noise reduction approach on the training instances gives better results in prediction with an improvement of 37% on precision, 70% on recall, and 59% on f-score.
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- Al-Sabbagh, Khaled, et al.
(author)
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Improving test case selection by handling class and attribute noise
- 2022
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In: Journal of Systems and Software. - : Elsevier BV. - 0164-1212. ; 183
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Journal article (peer-reviewed)abstract
- Big data and machine learning models have been increasingly used to support software engineering processes and practices. One example is the use of machine learning models to improve test case selection in continuous integration. However, one of the challenges in building such models is the large volume of noise that comes in data, which impedes their predictive performance. In this paper, we address this issue by studying the effect of two types of noise, called class and attribute, on the predictive performance of a test selection model. For this purpose, we analyze the effect of class noise by using an approach that relies on domain knowledge for relabeling contradictory entries and removing duplicate ones. Thereafter, an existing approach from the literature is used to experimentally study the effect of attribute noise removal on learning. The analysis results show that the best learning is achieved when training a model on class-noise cleaned data only - irrespective of attribute noise. Specifically, the learning performance of the model reported 81% precision, 87% recall, and 84% f-score compared with 44% precision, 17% recall, and 25% f-score for a model built on uncleaned data. Finally, no causality relationship between attribute noise removal and the learning of a model for test case selection was drawn. (C) 2021 The Author(s). Published by Elsevier Inc.
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