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- 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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Predicting Test Case Verdicts Using TextualAnalysis of Commited Code Churns
- 2019
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Ingår i: CEUR Workshop Proceedings. - 1613-0073. ; 2476, s. 138-153
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Konferensbidrag (refereegranskat)abstract
- Background: Continuous Integration (CI) is an agile software development practice that involves producing several clean builds of the software per day. The creation of these builds involve running excessive executions of automated tests, which is hampered by high hardware cost and reduced development velocity. Goal: The goal of our research is to develop a method that reduces the number of executed test cases at each CI cycle.Method: We adopt a design research approach with an infrastructure provider company to develop a method that exploits Ma-chine Learning (ML) to predict test case verdicts for committed sourcecode. We train five different ML models on two data sets and evaluate their performance using two simple retrieval measures: precision and recall. Results: While the results from training the ML models on the first data-set of test executions revealed low performance, the curated data-set for training showed an improvement on performance with respect to precision and recall. Conclusion: Our results indicate that the method is applicable when training the ML model on churns of small sizes
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| 3. |
- 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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| 6. |
- Antinyan, Vard, 1984, et al.
(författare)
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Identifying Complex Functions : By Investigating Various Aspects of Code Complexity
- 2015
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Ingår i: Proceedings of 2015 Science and Information Conference (SAI). - : IEEE Press. - 9781479985470 - 9781479985487 - 9781479985463 ; , s. 879-888
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Konferensbidrag (refereegranskat)abstract
- The complexity management of software code has become one of the major problems in software development industry. With growing complexity the maintenance effort of code increases. Moreover, various aspects of complexity create difficulties for complexity assessment. The objective of this paper is to investigate the relationships of various aspects of code complexity and propose a method for identifying the most complex functions. We have conducted an action research project in two software development companies and complemented it with a study of three open source products. Four complexity metrics are measured, and their nature and mutual influence are investigated. The results and possible explanations are discussed with software engineers in industry. The results show that there are two distinguishable aspects of complexity of source code functions: Internal and outbound complexities. Those have an inverse relationship. Moreover, the product of them does not seem to be greater than a certain limit, regardless of software size. We present a method that permits identification of most complex functions considering the two aspects of complexities. The evaluation shows that the use of the method is effective in industry: It enables identification of 0.5% most complex functions out of thousands of functions for reengineering.
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| 7. |
- Antinyan, Vard, 1984, et al.
(författare)
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Identifying risky areas of software code in Agile/Lean software development: An industrial experience report
- 2014
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Ingår i: 2014 Software Evolution Week - IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering, CSMR-WCRE 2014 - Proceedings. - : IEEE. - 9781479941742
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Konferensbidrag (refereegranskat)abstract
- Modern software development relies on incremental delivery to facilitate quick response to customers' requests. In this dynamic environment the continuous modifications of software code can cause risks for software developers; when developing a new feature increment, the added or modified code may contain fault-prone or difficult-to-maintain elements. The outcome of these risks can be defective software or decreased development velocity. This study presents a method to identify the risky areas and assess the risk when developing software code in Lean/Agile environment. We have conducted an action research project in two large companies, Ericsson AB and Volvo Group Truck Technology. During the study we have measured a set of code properties and investigated their influence on risk. The results show that the superposition of two metrics, complexity and revisions of a source code file, can effectively enable identification and assessment of the risk. We also illustrate how this kind of assessment can be successfully used by software developers to manage risks on a weekly basis as well as release-wise. A measurement system for systematic risk assessment has been introduced to two companies. © 2014 IEEE.
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| 8. |
- Antinyan, Vard, 1984, et al.
(författare)
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Monitoring Evolution of Code Complexity and Magnitude of Changes
- 2014
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Ingår i: Acta Cybernetica. - 0324-721X. ; 21:3, s. 367-382
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Tidskriftsartikel (refereegranskat)abstract
- Background: Complexity management has become a crucial activity in continuous software development. While the overall perceived complexity of a product grows rather insignificantly, the small units, such as functions and files, can have noticeable complexity growth with every increment of product features. This kind of evolution triggers risks of escalating fault-proneness and deteriorating maintainability. Goal: The goal of this research was to develop a measurement system which enables effective monitoring of complexity evolution. Method: An action research has been conducted in two large software development organiza-tions. We have measured three complexity and two change properties of code for two large industrial products. The complexity growth has been measured for five consecutive releases of products. Different patterns of growth have been identified and evaluated with software engi-neers in industry. Results: The results show that monitoring cyclomatic complexity evolution of functions and number of revisions of files focuses the attention of designers to potentially problematic files and functions for manual assessment and improvement. A measurement system was developed at Ericsson to support the monitoring process.
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| 9. |
- Antinyan, Vard, et al.
(författare)
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Monitoring evolution of code complexity and magnitude of changes
- 2014
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Ingår i: Acta Cybernetica. - : University of Szeged, Institute of Informatics. - 0324-721X. ; 21:3, s. 367-382
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Tidskriftsartikel (refereegranskat)abstract
- Complexity management has become a crucial activity in continuous software development. While the overall perceived complexity of a product grows rather insignificantly, the small units, such as functions and files, can have noticeable complexity growth with every increment of product features. This kind of evolution triggers risks of escalating fault-proneness and deteriorating maintainability. The goal of this research was to develop a measurement system which enables effective monitoring of complexity evolution. An action research has been conducted in two large software development organizations. We have measured three complexity and two change properties of code for two large industrial products. The complexity growth has been measured for five consecutive releases of the products. Different patterns of growth have been identified and evaluated with software engineers in industry. The results show that monitoring cyclomatic complexity evolution of functions and number of revisions of files focuses the attention of designers to potentially problematic files and functions for manual assessment and improvement. A measurement system was developed at Ericsson to support the monitoring process.
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| 10. |
- Antinyan, Vard, 1984, et al.
(författare)
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Monitoring Evolution of Code Complexity in Agile/Lean Software Development - A Case Study at Two Companies
- 2013
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Ingår i: 13th Symposium on Programming Languages and Software Tools, SPLST 2013 - Proceedings. - 9789633062289 ; , s. 1-15
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Konferensbidrag (refereegranskat)abstract
- One of the distinguishing characteristics of Agile and Lean software development is that software products “grow” with new functionality with relatively small increments. Continuous customer demands of new features and the companies’ abilities to deliver on those demands are the two driving forces behind this kind of software evolution. Despite the numerous benefits there are a number of risks associated with this kind of growth. One of the main risks is the fact that the complexity of the software product grows slowly, but over time reaches scales which makes the product hard to maintain or evolve. The goal of this paper is to present a measurement system for monitoring the growth of complexity and drawing attention when it becomes problematic. The measurement system was developed during a case study at Ericsson and Volvo Group Truck Technology. During the case study we explored the evolution of size, complexity, revisions and number of designers of two large software products from the telecom and automotive domains. The results show that two measures needed to be monitored to keep the complexity development under control - McCabe’s complexity and number of revisions.
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