Elixir: Effective object-oriented program repair

Saha, Ripon K.; Lyu, Yingjun; Yoshida, Hiroaki; Prasad, Munaga V. N. K.

doi:10.1109/ase.2017.8115675

Cited by 136 publications

(90 citation statements)

References 31 publications

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“…We now compare those results against the patches found by recent program repair tools that are publicly available. Elixir [4], CapGen [3] and SimFix [31] have reported 26, 22, 34 correctly repaired bugs for all Defects4J bugs, where the patch is identical to the human patch or claimed as correct. Of those correctly repaired bugs, 22, 19 and 17 respectively are for the 75 one-line bugs that we consider for SEQUENCER.…”

Section: Answer To Rq3: Defects4j Evaluationmentioning

confidence: 99%

“…Program repair research is very active and dominated by techniques based on static analysis (e.g., Angelix [2]) and dynamic analysis (e.g., CapGen [3]). While great progress has been achieved, the current state of automated program repair is limited to simple small fixes, mostly one line patches [3], [4]. These techniques are heavily top-down, based on intelligent design and domain-specific knowledge about bug fixing in a given language or a specific application domain.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair

Chen

Kommrusch

Tufano

et al. 2021

IIEEE Trans. Software Eng.

199

346

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This paper presents a novel end-to-end approach to program repair based on sequence-to-sequence learning. We devise, implement, and evaluate a technique, called SEQUENCER, for fixing bugs based on sequence-to-sequence learning on source code. This approach uses the copy mechanism to overcome the unlimited vocabulary problem that occurs with big code. Our system is data-driven; we train it on 35,578 samples, carefully curated from commits to open-source repositories. We evaluate SEQUENCER on 4,711 independent real bug fixes, as well on the Defects4J benchmark used in program repair research. SEQUENCER is able to perfectly predict the fixed line for 950/4,711 testing samples, and find correct patches for 14 bugs in Defects4J benchmark. SEQUENCER captures a wide range of repair operators without any domain-specific top-down design.Index Terms-program repair; machine learning. ! Zimin Chen is currently a PhD student at KTH Royal Institute of Technology. He also received the BS and MS degree in computer science from KTH. His research interest lies in the intersection between machine learning and software engineering, especially between automatic program repair and machine learning.Steve Kommrusch is currently a PhD candidate focused on machine learning at Colorado State University. He received his BS in computer engineering from University of Illinois in 1987 and his MS in EECS from MIT in 1989. From 1989 through 2017, he worked in industry at Hewlett-Packard, National Semiconductor, and Advanced Micro Devices. Steve holds over 30 patents in the fields of computer graphics algorithms, silicon simulation and debug techniques, and silicon performance and power management. His research interests include Program Equivalence, Program Repair, and Constructivist AI using machine learning. Electrical and Computer Engineering department. He is working on patternspecific languages and compilers for scientific computing, and has designed numerous approaches using optimizing compilation to effectively map applications to CPUs, GPUs, FPGAs and System-on-Chips. His work spans a variety of domains including compiler optimization design especially in the polyhedral compilation framework, high-level synthesis for FPGAs and SoCs, and distributed computing. Previously

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Section: Answer To Rq3: Defects4j Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair

Chen

Kommrusch

Tufano

et al. 2021

IIEEE Trans. Software Eng.

199

346

View full text Add to dashboard Cite

show abstract

“…We observed that -7 patterns are compatible with fix patterns that are mined manually from bug fix patches (i.e., fix patterns in PAR [33]). between 1 and 8 patterns are compatible with researcher-predefined fix patterns used in ssFix [93], ELIXIR [81], S3 [41], NEPfix [15], and Sketch-Fix [27], respectively. -7 patterns are compatible with fix pattern mined from history bug fixes by HDRepair [43], 9 patterns are compatible with fix patterns mined from StackOverflow by SOFix [54], and 1 fix pattern is compatible with 1 fix pattern mined by Genesis [56] that focuses on mining fix patterns for three kinds of bugs.…”

Section: Rq2: Compatibility Between Fixminer's Patterns and Apr Litermentioning

confidence: 80%

“…A common and reliable strategy in automatic program repair is to generate concrete patches based on fix patterns [33] (also referred to as fix templates [54] or program transformation schemas [27]). Several APR systems [15,27,33,50,51,54,63,81] in the literature implement this strategy by using diverse sets of fix patterns obtained either via manual generation or automatic mining of bug fix datasets.…”

Section: Introductionmentioning

confidence: 99%

FixMiner: Mining relevant fix patterns for automated program repair

et al. 2020

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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 ASTlevel 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

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“…Nevertheless, its fix patterns, except the fix templates from PAR, still limits the code change actions at abstract syntax tree (AST) node level, but are not specific for some types of bugs. ELIXIR [40] aggressively uses method call related templates from PAR with local variables, fields, or constants, to construct more expressive repair-expressions that go into synthesizing patches.…”

Section: Related Workmentioning

confidence: 99%

AVATAR: Fixing Semantic Bugs with Fix Patterns of Static Analysis Violations

Liu

Koyuncu

Kim

et al. 2019

2019 IEEE 26th International Conference on Software Analysis, Evolution and Reengineering (SANER)

116

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Fix pattern-based patch generation is a promising direction in Automated Program Repair (APR). Notably, it has been demonstrated to produce more acceptable and correct patches than the patches obtained with mutation operators through genetic programming. The performance of pattern-based APR systems, however, depends on the fix ingredients mined from fix changes in development histories. Unfortunately, collecting a reliable set of bug fixes in repositories can be challenging. In this paper, we propose to investigate the possibility in an APR scenario of leveraging code changes that address violations by static bug detection tools. To that end, we build the AVATAR APR system, which exploits fix patterns of static analysis violations as ingredients for patch generation. Evaluated on the Defects4J benchmark, we show that, assuming a perfect localization of faults, AVATAR can generate correct patches to fix 34/39 bugs. We further find that AVATAR yields performance metrics that are comparable to that of the closely-related approaches in the literature. While AVATAR outperforms many of the state-of-theart pattern-based APR systems, it is mostly complementary to current approaches. Overall, our study highlights the relevance of static bug finding tools as indirect contributors of fix ingredients for addressing code defects identified with functional test cases.Index Terms-Automated program repair, static analysis, fix pattern.

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Elixir: Effective object-oriented program repair

Cited by 136 publications

References 31 publications

SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair

SEQUENCER: Sequence-to-Sequence Learning for End-to-End Program Repair

FixMiner: Mining relevant fix patterns for automated program repair

AVATAR: Fixing Semantic Bugs with Fix Patterns of Static Analysis Violations

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