| 1. |
- Al Sabbagh, Khaled, 1987, et al.
(författare)
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Improving Data Quality for Regression Test Selection by Reducing Annotation Noise
- 2020
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Ingår i: Proceedings - 46th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2020. ; , s. 191-194
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Konferensbidrag (refereegranskat)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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| 2. |
- Al Sabbagh, Khaled, 1987, et al.
(författare)
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Selective Regression Testing based on Big Data: Comparing Feature Extraction Techniques
- 2020
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Ingår i: IEEE Software. - 1937-4194 .- 0740-7459. ; , s. 322-329
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Konferensbidrag (refereegranskat)abstract
- Regression testing is a necessary activity in continuous integration (CI) since it provides confidence that modified parts of the system are correct at each integration cycle. CI provides large volumes of data which can be used to support regression testing activities. By using machine learning, patterns about faulty changes in the modified program can be induced, allowing test orchestrators to make inferences about test cases that need to be executed at each CI cycle. However, one challenge in using learning models lies in finding a suitable way for characterizing source code changes and preserving important information. In this paper, we empirically evaluate the effect of three feature extraction algorithms on the performance of an existing ML-based selective regression testing technique. We designed and performed an experiment to empirically investigate the effect of Bag of Words (BoW), Word Embeddings (WE), and content-based feature extraction (CBF). We used stratified cross validation on the space of features generated by the three FE techniques and evaluated the performance of three machine learning models using the precision and recall metrics. The results from this experiment showed a significant difference between the models' precision and recall scores, suggesting that the BoW-fed model outperforms the other two models with respect to precision, whereas a CBF-fed model outperforms the rest with respect to recall.
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| 3. |
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| 4. |
- Berbyuk Lindström, Nataliya, 1978, et al.
(författare)
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Understanding Metrics Team-Stakeholder Communication in Agile Metrics Service Delivery
- 2021
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Ingår i: APSEC (Asian Pacific Software Engineering conference), December 6-10, Taiwan-Virtual.. ; 2021-December, s. 401-409
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Konferensbidrag (refereegranskat)abstract
- In this paper, we explore challenges in communication between metrics teams and stakeholders in metrics service delivery. Drawing on interviews and interactive workshops with team members and stakeholders at two different Swedish agile software development organizations, we identify interrelated challenges such as aligning expectations, prioritizing demands, providing regular feedback, and maintaining continuous dialogue, which influence team-stakeholder interaction, relationships and performance. Our study shows the importance of understanding communicative hurdles and provides suggestions for their mitigation, therefore meriting further empirical research.
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| 5. |
- Horkoff, Jennifer, 1980, et al.
(författare)
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A Method for Modeling Data Anomalies in Practice
- 2021
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Ingår i: Proceedings - 2021 47th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2021.
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Konferensbidrag (refereegranskat)abstract
- As technology has allowed us to collect large amounts of industrial data, it has become critical to analyze and understand the data collected, in particular to find data anomalies. Anomaly analysis allows a company to detect, analyze and understand anomalous or unusual data patterns. This is an important activity to understand, for example, deviations in service which may indicate potential problems, or differing customer behavior which may reveal new business opportunities. Much previous work has focused on anomaly detection, in particular using machine learning. Such approaches allow clustering of data patterns by common attributes, and, although useful, clusters often do not correspond to the root causes of anomalies, meaning that more manual analysis is needed. In this paper we report on a design science study with two different teams, in a partner company which focuses on modeling and understanding the attributes and root causes of data anomalies. After iteration, for each team, we have created general and anomaly-specific UML class diagrams and goal models to capture anomaly details. We use our experiences to create an example taxonomy, classifying anomalies by their root causes, and to create a general method for modeling and understanding data anomalies. This work paves the way for a better understanding of anomalies and their root causes, leading towards creating a training set which may be used for machine learning approaches.
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| 6. |
- Ochodek, M., et al.
(författare)
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Automated Code Review Comment Classification to Improve Modern Code Reviews
- 2022
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Ingår i: Lecture Notes in Business Information Processing. - Cham : Springer International Publishing. - 1865-1356 .- 1865-1348. ; 439 LNBIP, s. 23-40
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Konferensbidrag (refereegranskat)abstract
- Modern Code Reviews (MCRs) are a widely-used quality assurance mechanism in continuous integration and deployment. Unfortunately, in medium and large projects, the number of changes that need to be integrated, and consequently the number of comments triggered during MCRs could be overwhelming. Therefore, there is a need for quickly recognizing which comments are concerning issues that need prompt attention to guide the focus of the code authors, reviewers, and quality managers. The goal of this study is to design a method for automated classification of review comments to identify the needed change faster and with higher accuracy. We conduct a Design Science Research study on three open-source systems. We designed a method (CommentBERT) for automated classification of the code-review comments based on the BERT (Bidirectional Encoder Representations from Transformers) language model and a new taxonomy of comments. When applied to 2,672 comments from Wireshark, The Mono Framework, and Open Network Automation Platform (ONAP) projects, the method achieved accuracy, measured using Matthews Correlation Coefficient, of 0.46–0.82 (Wireshark), 0.12–0.8 (ONAP), and 0.48–0.85 (Mono). Based on the results, we conclude that the proposed method seems promising and could be potentially used to build machine-learning-based tools to support MCRs as long as there is a sufficient number of historical code-review comments to train the model.
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| 7. |
- Ochodek, Miroslaw, et al.
(författare)
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On Identifying Similarities in Git Commit Trends—A Comparison Between Clustering and SimSAX
- 2020
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Ingår i: SWQD 2020: Software Quality: Quality Intelligence in Software and Systems Engineering. - Cham : Springer. - 1865-1348 .- 1865-1356. - 9783030355104
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Konferensbidrag (refereegranskat)abstract
- Software products evolve increasingly fast as markets continuously demand new features and agility to customer’s need. This evolution of products triggers an evolution of software development practices in a different way. Compared to classical methods, where products were developed in projects, contemporary methods for continuous integration, delivery, and deployment develop products as part of continuous programs. In this context, software architects, designers, and quality engineers need to understand how the processes evolve over time since there is no natural start and stop of projects. For example, they need to know how similar two iterations of the same program or how similar two development programs are. In this paper, we compare three methods for calculating the degree of similarity between projects by comparing their Git commit series. We test three approaches—the DNA-motifs-inspired …
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| 8. |
- Ochodek, Miroslaw, et al.
(författare)
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Recognizing lines of code violating company-specific coding guidelines using machine learning A Method and Its Evaluation
- 2020
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Ingår i: Empirical Software Engineering. - : Springer Science and Business Media LLC. - 1382-3256 .- 1573-7616. ; 25, s. 220-265
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Tidskriftsartikel (refereegranskat)abstract
- Software developers in big and medium-size companies are working with millions of lines of code in their codebases. Assuring the quality of this code has shifted from simple defect management to proactive assurance of internal code quality. Although static code analysis and code reviews have been at the forefront of research and practice in this area, code reviews are still an effort-intensive and interpretation-prone activity. The aim of this research is to support code reviews by automatically recognizing company-specific code guidelines violations in large-scale, industrial source code. In our action research project, we constructed a machine-learning-based tool for code analysis where software developers and architects in big and medium-sized companies can use a few examples of source code lines violating code/design guidelines (up to 700 lines of code) to train decision-tree classifiers to find similar …
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| 9. |
- Staron, Miroslaw, 1977, et al.
(författare)
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Ensuring Sustainability of Knowledge
- 2020
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Ingår i: Action Research in Software Engineering. ; , s. 153-168
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Bokkapitel (övrigt vetenskapligt/konstnärligt)
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| 10. |
- Staron, Miroslaw, 1977, et al.
(författare)
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Improving Quality of Code Review Datasets – Token-Based Feature Extraction Method
- 2021
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Ingår i: Lecture Notes in Business Information Processing. - Cham : Springer International Publishing. - 1865-1356 .- 1865-1348. ; 404, s. 81-93
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Konferensbidrag (refereegranskat)abstract
- Machine learning is used increasingly frequent in software engineering to automate tasks and improve the speed and quality of software products. One of the areas where machine learning starts to be used is the analysis of software code. The goal of this paper is to evaluate a new method for creating machine learning feature vectors, based on the content of a line of code. We designed a new feature extraction algorithm and evaluated it in an industrial case study. Our results show that using the new feature extraction technique improves the overall performance in terms of MCC (Matthews Correlation Coefficient) by 0.39 – from 0.31 to 0.70, while reducing the precision by 0.05. The implications of this is that we can improve overall prediction accuracy for both true positives and true negatives significantly. This increases the trust in the predictions by the practitioners and contributes to its deeper adoption in practice.
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