A deeper look into bug fixes

Soto, Mauricio; Thung, Ferdian; Wong, Chu-Pan; Goues, Claire Le; Lo, David

doi:10.1145/2901739.2903495

Cited by 28 publications

(10 citation statements)

References 15 publications

(21 reference statements)

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“…Getafix implicitly uses a similar prioritization using its clustering and ranking strategy. Soto et al [2016] observe that fix patterns differ across programming languages and analyze the structure of typical patterns in Java. Martinez et al [Martinez and Monperrus 2012 also performed extensive AST analysis on Java repositories to statistically analyze code change patterns, which can guide program repair.…”

Section: Related Work 81 Automated Program Repairmentioning

confidence: 99%

Getafix: learning to fix bugs automatically

Bader

Scott

Pradel

et al. 2019

Proc. ACM Program. Lang.

191

121

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Static analyzers help find bugs early by warning about recurring bug categories. While fixing these bugs still remains a mostly manual task in practice, we observe that fixes for a specific bug category often are repetitive. This paper addresses the problem of automatically fixing instances of common bugs by learning from past fixes. We present Getafix, an approach that produces human-like fixes while being fast enough to suggest fixes in time proportional to the amount of time needed to obtain static analysis results in the first place. Getafix is based on a novel hierarchical clustering algorithm that summarizes fix patterns into a hierarchy ranging from general to specific patterns. Instead of an expensive exploration of a potentially large space of candidate fixes, Getafix uses a simple yet effective ranking technique that uses the context of a code change to select the most appropriate fix for a given bug. Our evaluation applies Getafix to 1,268 bug fixes for six bug categories reported by popular static analyzers for Java, including null dereferences, incorrect API calls, and misuses of particular language constructs. The approach predicts exactly the human-written fix as the topmost suggestion between 12% and 91% of the time, depending on the bug category. The top-5 suggestions contain fixes for 526 of the 1,268 bugs. Moreover, we report on deploying the approach within Facebook, where it contributes to the reliability of software used by billions of people. To the best of our knowledge, Getafix is the first industrially-deployed automated bug-fixing tool that learns fix patterns from past, human-written fixes to produce human-like fixes. CCS Concepts: • Software and its engineering → Software testing and debugging.

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Section: Related Work 81 Automated Program Repairmentioning

confidence: 99%

Getafix: learning to fix bugs automatically

Bader

Scott

Pradel

et al. 2019

Proc. ACM Program. Lang.

191

121

View full text Add to dashboard Cite

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“…Cam-pos and Maia [4] conduct an empirical analysis to characterize bug-fix patterns in Java open-source repositories. Soto et al [21] leverage lessons learned in C projects and perform a large-scale study of bug-fix patterns of Java projects on GitHub.…”

Section: Classical Bug and Fix Patternsmentioning

confidence: 99%

Automatic Diagnosis of Quantum Software Bug Fix Motifs

Kher,

Joshi,

Mukkavalli

et al. 2023

International Conferences on Software Engineering and Knowledge Engineering

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Bug-fix pattern detection has been investigated in the past in the context of classical software. However, while quantum software is developing rapidly, the literature is still lacking automated methods and tools to identify, analyze, and detect bug-fix patterns. To the best of our knowledge, our work is the first to leverage classical techniques to detect bug-fix patterns in quantum code. In this paper, we propose an automated framework, called Q-Diff, for detecting bug-fix patterns in IBM Qiskit quantum code. In the framework, we develop a proof-of-concept tool based on Abstract Syntax Trees. To validate our method, we test Q-Diff with a variety of quantum bug-fix patterns using examples. We hope our work will attract the attention of the quantum software engineering community to improve the quality of quantum software.

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“…There are many valuable pieces of information stored in a version control system of a project; they include: source code, documentation, issue reports, test cases, list of contributors, etc. Researchers mine these software repositories to get useful insights related to how bugs are fixed [1], how developers collaborate [2] and so on. With the abundance of the open source repositories in GitHub, researchers can mine for insights and validate hypotheses on a large corpus of data.…”

Section: Introductionmentioning

confidence: 99%

“…They only eliminate tutorials, homework assignments and so on. We make our dataset publicly available 1 .…”

Section: Introductionmentioning

confidence: 99%

“…the code is split into different modules and no ad-hoc scripts; (3) check whether good documentations are provided; (4) check if the project uses issues to track new features and bugs; (5) check if the project uses a CI service, e.g. Travis, CircleCI, etc; (6) check if the project is updated within the last one month;(7) check how many active contributors the project has; (8) check whether the project provides a license.For every point in the guideline, we consider the following dimensions for the project assessment: unit testing for point(1), architecture for point(2), documentation for point (3), issues for point (4), CI for point(5), history for point(6), community for point(7), and license for point(8). Aside from providing a label on whether a project is engineered or not, the labellers also provide descriptive information for every dimension.…”

mentioning

confidence: 99%

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NICHE: A Curated Dataset of Engineered Machine Learning Projects in Python

Widyasari,

YANG,

Thung

et al. 2023

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Machine learning (ML) has gained much attention and been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such high-quality dataset poses an obstacle in understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on evidences of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. This dataset can help researchers understand the practices that are followed in high-quality ML projects. It can also be used as a benchmark for classifiers designed to identify engineered ML projects.

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A deeper look into bug fixes

Cited by 28 publications

References 15 publications

Getafix: learning to fix bugs automatically

Getafix: learning to fix bugs automatically

Automatic Diagnosis of Quantum Software Bug Fix Motifs

NICHE: A Curated Dataset of Engineered Machine Learning Projects in Python

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