| 1. |
- Baudry, Benoit, et al.
(author)
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A Software-Repair Robot Based on Continual Learning
- 2021
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In: IEEE Software. - : Institute of Electrical and Electronics Engineers (IEEE). - 0740-7459 .- 1937-4194. ; 38:4, s. 28-35
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Journal article (peer-reviewed)abstract
- Software bugs are common, and correcting them accounts for a significant portion of the costs in the software development and maintenance process. In this article, we discuss R-Hero, our novel system for learning how to fix bugs based on continual training.
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| 2. |
- Borg, Markus, et al.
(author)
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Human, What Must I Tell You?
- 2023
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In: IEEE Software. - : Institute of Electrical and Electronics Engineers (IEEE). - 0740-7459 .- 1937-4194. ; 40:3, s. 9-14
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Journal article (peer-reviewed)abstract
- Artificial intelligence (AI)-assisted code generation is everywhere these days. Undoubtedly, AI will help near-future developers substantially by providing code suggestions and automation. In this application, explainability will be a key quality attribute. But what needs to be explained to whom? And how to deliver the explanations nonintrusively?
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| 3. |
- Etemadi, Khashayar, et al.
(author)
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Augmenting Diffs With Runtime Information
- 2023
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In: IEEE Transactions on Software Engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 0098-5589 .- 1939-3520. ; 49:11, s. 4988-5007
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Journal article (peer-reviewed)abstract
- Source code diffs are used on a daily basis as part of code review, inspection, and auditing. To facilitate understanding, they are typically accompanied by explanations that describe the essence of what is changed in the program. As manually crafting high-quality explanations is a cumbersome task, researchers have proposed automatic techniques to generate code diff explanations. Existing explanation generation methods solely focus on static analysis, i.e., they do not take advantage of runtime information to explain code changes. In this article, we propose Collector-Sahab, a novel tool that augments code diffs with runtime difference information. Collector-Sahab compares the program states of the original (old) and patched (new) versions of a program to find unique variable values. Then, Collector-Sahab adds this novel runtime information to the source code diff as shown, for instance, in code reviewing systems. As an evaluation, we run Collector-Sahab on 584 code diffs for Defects4J bugs and find it successfully augments the code diff for 95% (555/584) of them. We also perform a user study and ask eight participants to score the augmented code diffs generated by Collector-Sahab. Per this user study, we conclude that developers find the idea of adding runtime data to code diffs promising and useful. Overall, our experiments show the effectiveness and usefulness of Collector-Sahab in augmenting code diffs with runtime difference information. Publicly-available repository: https://github.com/ASSERT-KTH/collector-sahab.
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| 4. |
- Etemadi, Khashayar, et al.
(author)
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Estimating the potential of program repair search spaces with commit analysis
- 2022
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In: Journal of Systems and Software. - : Elsevier BV. - 0164-1212 .- 1873-1228. ; 188
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Journal article (peer-reviewed)abstract
- The most natural method for evaluating program repair systems is to run them on bug datasets, such as Defects4J. Yet, using this evaluation technique on arbitrary real-world programs requires heavy configuration. In this paper, we propose a purely static method to evaluate the potential of the search space of repair approaches. This new method enables researchers and practitioners to encode the search spaces of repair approaches and select potentially useful ones without struggling with tool configuration and execution. We encode the search spaces by specifying the repair strategies they employ. Next, we use the specifications to check whether past commits lie in repair search spaces. For a repair approach, including many human-written past commits in its search space indicates its potential to generate useful patches. We implement our evaluation method in LIGHTER. LIGHTER gets a Git repository and outputs a list of commits whose source code changes lie in repair search spaces. We run LIGHTER on 55,309 commits from the history of 72 Github repositories with and show that LIGHTER's precision and recall are 77% and 92%, respectively. Overall, our experiments show that our novel method is both lightweight and effective to study the search space of program repair approaches.
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| 5. |
- Etemadi, Khashayar, et al.
(author)
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On the Relevance of Cross-project Learning with Nearest Neighbours for Commit Message Generation
- 2020
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In: Proceedings - 2020 IEEE/ACM 42nd International Conference on Software Engineering Workshops, ICSEW 2020. - New York, NY, USA : Association for Computing Machinery, Inc. ; , s. 470-475
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Conference paper (peer-reviewed)abstract
- Commit messages play an important role in software maintenance and evolution. Nonetheless, developers often do not produce high-quality messages. A number of commit message generation methods have been proposed in recent years to address this problem. Some of these methods are based on neural machine translation (NMT) techniques. Studies show that the nearest neighbor algorithm (NNGen) outperforms existing NMT-based methods, although NNGen is simpler and faster than NMT. In this paper, we show that NNGen does not take advantage of cross-project learning in the majority of the cases. We also show that there is an even simpler and faster variation of the existing NNGen method which outperforms it in terms of the BLEU_4 score without using cross-project learning.
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| 6. |
- Etemadi, Khashayar, et al.
(author)
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Sorald : Automatic Patch Suggestions for SonarQube Static Analysis Violations
- 2022
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In: IEEE Transactions on Dependable and Secure Computing. - : Institute of Electrical and Electronics Engineers (IEEE). - 1545-5971 .- 1941-0018. ; , s. 1-1
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Journal article (peer-reviewed)abstract
- Previous work has shown that early resolution of issues detected by static code analyzers can prevent major costs later on. However, developers often ignore such issues for two main reasons. First, many issues should be interpreted to determine if they correspond to actual flaws in the program. Second, static analyzers often do not present the issues in a way that is actionable. To address these problems, we present Sorald: a novel system that uses metaprogramming templates to transform the abstract syntax trees of programs and suggests fixes for static analysis warnings. Thus, the burden on the developer is reduced from interpreting and fixing static issues, to inspecting and approving full fledged solutions. Sorald fixes violations of 10 rules from SonarJava, one of the most widely used static analyzers for Java. We evaluate Sorald on a dataset of 161 popular repositories on Github. Our analysis shows the effectiveness of Sorald as it fixes 65% (852/1,307) of the violations that meets the repair preconditions. Overall, our experiments show it is possible to automatically fix notable violations of the static analysis rules produced by the state-of-the-art static analyzer SonarJava.
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