| 1. |
- Cabrera Arteaga, Javier, 1992-, et al.
(författare)
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Superoptimization of WebAssembly bytecode
- 2020
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Ingår i: Conference Companion of the 4th International Conference on Art, Science, and Engineering of Programming. - Portugal : Aakar Books.
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Konferensbidrag (refereegranskat)abstract
- Motivated by the fast adoption of WebAssembly, we propose the first functional pipeline to support the superoptimization of WebAssembly bytecode. Our pipeline works over LLVM and Souper. We evaluate our superoptimization pipeline with 12 programs from the Rosetta code project. Our pipeline improves the code section size of 8 out of 12 programs. We discuss the challenges faced in superoptimization of WebAssembly with two case studies.
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| 2. |
- Danglot, Benjamin, et al.
(författare)
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An approach and benchmark to detect behavioral changes of commits in continuous integration
- 2020
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Ingår i: Empirical Software Engineering. - : Springer Nature. - 1382-3256 .- 1573-7616. ; 25:4, s. 2379-2415
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Tidskriftsartikel (refereegranskat)abstract
- When a developer pushes a change to an application’s codebase, a good practice is to have a test case specifying this behavioral change. Thanks to continuous integration (CI), the test is run on subsequent commits to check that they do no introduce a regression for that behavior. In this paper, we propose an approach that detects behavioral changes in commits. As input, it takes a program, its test suite, and a commit. Its output is a set of test methods that capture the behavioral difference between the pre-commit and post-commit versions of the program. We call our approach DCI (Detecting behavioral changes in CI). It works by generating variations of the existing test cases through (i) assertion amplification and (ii) a search-based exploration of the input space. We evaluate our approach on a curated set of 60 commits from 6 open source Java projects. To our knowledge, this is the first ever curated dataset of real-world behavioral changes. Our evaluation shows that DCI is able to generate test methods that detect behavioral changes. Our approach is fully automated and can be integrated into current development processes. The main limitations are that it targets unit tests and works on a relatively small fraction of commits. More specifically, DCI works on commits that have a unit test that already executes the modified code. In practice, from our benchmark projects, we found 15.29% of commits to meet the conditions required by DCI.
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| 3. |
- Durieux, Thomas, et al.
(författare)
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Fully Automated HTML and JavaScript Rewriting for Constructing a Self-healing Web Proxy
- 2020
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Ingår i: Software testing, verification & reliability. - : WILEY. - 0960-0833 .- 1099-1689. ; 30:2
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Tidskriftsartikel (refereegranskat)abstract
- Over the last few years, the complexity of web applications has increased to provide more dynamic web applications to users. The drawback of this complexity is the growing number of errors in the front-end applications. In this paper, we present an approach to provide self-healing for the web. We implemented this approach in two different tools: (i) BikiniProxy, an HTTP repair proxy, and (ii) BugBlock, a browser extension. They use five self-healing strategies to rewrite the buggy HTML and JavaScript code to handle errors in web pages. We evaluate BikiniProxy and BugBlock with a new benchmark of 555 reproducible JavaScript errors of which 31.76% can be automatically self-healed by BikiniProxy and 15.67% by BugBlock.
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| 4. |
- Etemadi, Khashayar, et al.
(författare)
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On the Relevance of Cross-project Learning with Nearest Neighbours for Commit Message Generation
- 2020
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Ingår i: 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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Konferensbidrag (refereegranskat)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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| 5. |
- Harrand, Nicolas, et al.
(författare)
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Java decompiler diversity and its application to meta-decompilation
- 2020
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Ingår i: Journal of Systems and Software. - : Elsevier BV. - 0164-1212 .- 1873-1228. ; 168
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Tidskriftsartikel (refereegranskat)abstract
- During compilation from Java source code to bytecode, some information is irreversibly lost. In other words, compilation and decompilation of Java code is not symmetric. Consequently, decompilation, which aims at producing source code from bytecode, relies on strategies to reconstruct the information that has been lost. Different Java decompilers use distinct strategies to achieve proper decompilation. In this work, we hypothesize that the diverse ways in which bytecode can be decompiled has a direct impact on the quality of the source code produced by decompilers. In this paper, we assess the strategies of eight Java decompilers with respect to three quality indicators: syntactic correctness, syntactic distortion and semantic equivalence modulo inputs. Our results show that no single modern decompiler is able to correctly handle the variety of bytecode structures coming from real-world programs. The highest ranking decompiler in this study produces syntactically correct, and semantically equivalent code output for 84%, respectively 78%, of the classes in our dataset. Our results demonstrate that each decompiler correctly handles a different set of bytecode classes. We propose a new decompiler called Arlecchino that leverages the diversity of existing decompilers. To do so, we merge partial decompilation into a new one based on compilation errors. Arlecchino handles 37.6% of bytecode classes that were previously handled by no decompiler. We publish the sources of this new bytecode decompiler. (C) 2020 Published by Elsevier Inc.
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| 6. |
- Koyuncu, Anil, et al.
(författare)
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FixMiner : Mining relevant fix patterns for automated program repair
- 2020
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Ingår i: Empirical Software Engineering. - : Springer. - 1382-3256 .- 1573-7616. ; 25:3, s. 1980-2024
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Tidskriftsartikel (refereegranskat)abstract
- Patching is a common activity in software development. It is generally performed on a source code base to address bugs or add new functionalities. In this context, given the recurrence of bugs across projects, the associated similar patches can be leveraged to extract generic fix actions. While the literature includes various approaches leveraging similarity among patches to guide program repair, these approaches often do not yield fix patterns that are tractable and reusable as actionable input to APR systems. In this paper, we propose a systematic and automated approach to mining relevant and actionable fix patterns based on an iterative clustering strategy applied to atomic changes within patches. The goal of FixMiner is thus to infer separate and reusable fix patterns that can be leveraged in other patch generation systems. Our technique, FixMiner, leverages Rich Edit Script which is a specialized tree structure of the edit scripts that captures the AST-level context of the code changes. FixMiner uses different tree representations of Rich Edit Scripts for each round of clustering to identify similar changes. These are abstract syntax trees, edit actions trees, and code context trees. We have evaluated FixMiner on thousands of software patches collected from open source projects. Preliminary results show that we are able to mine accurate patterns, efficiently exploiting change information in Rich Edit Scripts. We further integrated the mined patterns to an automated program repair prototype, PAR(FixMiner), with which we are able to correctly fix 26 bugs of the Defects4J benchmark. Beyond this quantitative performance, we show that the mined fix patterns are sufficiently relevant to produce patches with a high probability of correctness: 81% of PAR(FixMiner)'s generated plausible patches are correct.
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