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Exploring and exploiting the correlations between bug-inducing and bug-fixing commits

https://doi.org/10.1145/3338906.3338962
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/smr.1619openalex-snapshot
Comparing text‐based and dependence‐based approaches for determining the origins of bugs
inferredcites →https://doi.org/10.1002/smr.1797openalex-snapshot
An empirical study on developer‐related factors characterizing fix‐inducing commits
inferredcites →https://doi.org/10.1007/bf02294183openalex-snapshot
Sample Size Requirements for Estimating Pearson, Kendall and Spearman Correlations
inferredcites →https://doi.org/10.1007/s10664-013-9282-8openalex-snapshot
Mining software repair models for reasoning on the search space of automated program fixing
inferredcites →https://doi.org/10.1007/s10664-015-9376-6openalex-snapshot
The impact of tangled code changes on defect prediction models
inferredcites →https://doi.org/10.1007/s10664-017-9567-4openalex-snapshot
ChangeLocator: locate crash-inducing changes based on crash reports
inferredcites →https://doi.org/10.1007/s11219-013-9208-0openalex-snapshot
Current challenges in automatic software repair
inferredcites →https://doi.org/10.1016/j.jss.2009.06.035openalex-snapshot
A practical evaluation of spectrum-based fault localization
inferredcites →https://doi.org/10.1037/a0028086openalex-snapshot
On effect size.
inferredcites →https://doi.org/10.1109/ase.2006.23openalex-snapshot
Automatic Identification of Bug-Introducing Changes
inferredcites →https://doi.org/10.1109/ase.2015.86openalex-snapshot
Do Automatically Generated Unit Tests Find Real Faults? An Empirical Study of Effectiveness and Challenges (T)
inferredcites →https://doi.org/10.1109/ase.2017.8115675openalex-snapshot
Elixir: Effective object-oriented program repair
inferredcites →https://doi.org/10.1109/ase.2017.8115676openalex-snapshot
Leveraging syntax-related code for automated program repair
inferredcites →https://doi.org/10.1109/csmr-wcre.2014.6747164openalex-snapshot
An empirical study of long lived bugs
inferredcites →https://doi.org/10.1109/csmr.2012.24openalex-snapshot
Do Developers Introduce Bugs When They Do Not Communicate? The Case of Eclipse and Mozilla
inferredcites →https://doi.org/10.1109/esem.2019.8870172openalex-snapshot
How Different Is It Between Machine-Generated and Developer-Provided Patches? : An Empirical Study on the Correct Patches Generated by Automated Program Repair Techniques
inferredcites →https://doi.org/10.1109/icse.2007.66openalex-snapshot
Predicting Faults from Cached History
inferredcites →https://doi.org/10.1109/icse.2017.62openalex-snapshot
Evaluating and Improving Fault Localization
inferredcites →https://doi.org/10.1109/icst.2019.00020openalex-snapshot
You Cannot Fix What You Cannot Find! An Investigation of Fault Localization Bias in Benchmarking Automated Program Repair Systems
inferredcites →https://doi.org/10.1109/msr.2013.6624018openalex-snapshot
The impact of tangled code changes
inferredcites →https://doi.org/10.1109/promise.2007.10openalex-snapshot
Predicting Defects for Eclipse
inferredcites →https://doi.org/10.1109/qrs.2015.14openalex-snapshot
Deep Learning for Just-in-Time Defect Prediction
inferredcites →https://doi.org/10.1109/scam.2012.20openalex-snapshot
When Does a Refactoring Induce Bugs? An Empirical Study
inferredcites →https://doi.org/10.1109/tse.2007.70773openalex-snapshot
Classifying Software Changes: Clean or Buggy?
inferredcites →https://doi.org/10.1109/tse.2012.43openalex-snapshot
Reducing Features to Improve Code Change-Based Bug Prediction
inferredcites →https://doi.org/10.1109/tse.2012.70openalex-snapshot
A large-scale empirical study of just-in-time quality assurance
inferredcites →https://doi.org/10.1109/tse.2016.2521368openalex-snapshot
A Survey on Software Fault Localization
inferredcites →https://doi.org/10.1109/tse.2016.2616306openalex-snapshot
A Framework for Evaluating the Results of the SZZ Approach for Identifying Bug-Introducing Changes
inferredcites →https://doi.org/10.1109/wcre.2011.31openalex-snapshot
How Long Does a Bug Survive? An Empirical Study
inferredcites →https://doi.org/10.1145/1081706.1081725openalex-snapshot
HATARI
inferredcites →https://doi.org/10.1145/1082983.1083147openalex-snapshot
When do changes induce fixes?
inferredcites →https://doi.org/10.1145/1095430.1081725openalex-snapshot
HATARI
inferredcites →https://doi.org/10.1145/1137983.1138027openalex-snapshot
How long did it take to fix bugs?
inferredcites →https://doi.org/10.1145/1294948.1294954openalex-snapshot
Learning from bug-introducing changes to prevent fault prone code
inferredcites →https://doi.org/10.1145/1882291.1882308openalex-snapshot
The missing links
inferredcites →https://doi.org/10.1145/1985441.1985464openalex-snapshot
Do time of day and developer experience affect commit bugginess?
inferredcites →https://doi.org/10.1145/1985793.1985860openalex-snapshot
Ownership, experience and defects
inferredcites →https://doi.org/10.1145/2025113.2025120openalex-snapshot
ReLink
inferredcites →https://doi.org/10.1145/2025113.2025121openalex-snapshot
How do fixes become bugs?
inferredcites →https://doi.org/10.1145/2393596.2393646openalex-snapshot
History slicing
inferredcites →https://doi.org/10.1145/2491411.2491430openalex-snapshot
Regression tests to expose change interaction errors
inferredcites →https://doi.org/10.1145/2597073.2597075openalex-snapshot
An empirical study of just-in-time defect prediction using cross-project models
inferredcites →https://doi.org/10.1145/2610384.2628055openalex-snapshot
Defects4J: a database of existing faults to enable controlled testing studies for Java programs
inferredcites →https://doi.org/10.1145/2610384.2628058openalex-snapshot
CoREBench: studying complexity of regression errors
inferredcites →https://doi.org/10.1145/2642937.2642982openalex-snapshot
Fine-grained and accurate source code differencing
inferredcites →https://doi.org/10.1145/2884781.2884835openalex-snapshot
On the techniques we create, the tools we build, and their misalignments
inferredcites →https://doi.org/10.1145/2931037.2948705openalex-snapshot
ASTOR: a program repair library for Java (demo)
inferredcites →https://doi.org/10.1145/2950290.2950297openalex-snapshot
Understanding and detecting wake lock misuses for Android applications
inferredcites →https://doi.org/10.1145/2950290.2950353openalex-snapshot
Effort-aware just-in-time defect prediction: simple unsupervised models could be better than supervised models
inferredcites →https://doi.org/10.1145/2970276.2970359openalex-snapshot
Locus: locating bugs from software changes
inferredcites →https://doi.org/10.1145/3092703.3092717openalex-snapshot
FLUCCS: using code and change metrics to improve fault localization
inferredcites →https://doi.org/10.1145/3180155.3180233openalex-snapshot
Context-aware patch generation for better automated program repair
inferredcites →https://doi.org/10.1145/3213846.3213871openalex-snapshot
Shaping program repair space with existing patches and similar code
inferredcites →https://doi.org/10.1145/3236024.3236056openalex-snapshot
Do the dependency conflicts in my project matter?
inferredcites →https://doi.org/10.1145/3319008.3319349openalex-snapshot
Attention Please
inferredcites →https://doi.org/10.1145/601858.601867openalex-snapshot
Foundations of statistical natural language processing
inferredcites →https://doi.org/10.11575/prism/46165openalex-snapshot
Bug introducing changes: A case study with Android
inferredcites →https://doi.org/10.1214/aoms/1177730491openalex-snapshot
On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other
inferredcites →https://doi.org/10.1364/josa.51.000248openalex-snapshot
Soviet Physics—Doklady
inferredcites →https://doi.org/10.5555/2486788.2487043openalex-snapshot
Identifying failure inducing developer pairs within developer networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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An Empirical Study on Real Bug Fixes in Smart Contracts Projects
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Leveraging structural properties of source code graphs for just-in-time bug prediction
inferred← citeshttps://doi.org/10.1007/s10664-022-10167-wopenalex-snapshot
A mixed-methods analysis of micro-collaborative coding practices in OpenStack
inferred← citeshttps://doi.org/10.1007/s10664-024-10479-zopenalex-snapshot
Hunting bugs: Towards an automated approach to identifying which change caused a bug through regression testing
inferred← citeshttps://doi.org/10.1007/s10664-024-10575-0openalex-snapshot
Prioritizing code review requests to improve review efficiency: a simulation study
inferred← citeshttps://doi.org/10.1007/s10664-025-10616-2openalex-snapshot
WIA-SZZ: Work item aware SZZ
inferred← citeshttps://doi.org/10.1007/s11219-022-09611-3openalex-snapshot
Utilizing source code syntax patterns to detect bug inducing commits using machine learning models
inferred← citeshttps://doi.org/10.1007/s11432-020-3317-2openalex-snapshot
Towards characterizing bug fixes through dependency-level changes in Apache Java open source projects
inferred← citeshttps://doi.org/10.1007/s11704-022-1729-xopenalex-snapshot
VulLoc: vulnerability localization based on inducing commits and fixing commits
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Comparative analysis of quantum and classical support vector classifiers for software bug prediction: an exploratory study
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Software bug prediction using entanglement-enhanced quantum support vector machines (E-QSVM)
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JIT-CF: Integrating contrastive learning with feature fusion for enhanced just-in-time defect prediction
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Technical debts and faults in open-source quantum software systems: An empirical study
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An empirical study on real bug fixes from solidity smart contract projects
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Coverage-Free LLM-Based Fault Localization via Semantic Reasoning Over Historical Bug Reports
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Neural SZZ Algorithm
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Vulnerability-Affected Versions Identification: How Far Are We?
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Fonte: Finding Bug Inducing Commits from Failures
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Flake Aware Culprit Finding
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SafeRevert: When Can Breaking Changes be Automatically Reverted?
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Speculative Testing at Google with Transition Prediction
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The Inversive Relationship Between Bugs and Patches: An Empirical Study
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ReFixar: Multi-version Reasoning for Automated Repair of Regression Errors
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Mea culpa: How developers fix their own simple bugs differently from other developers
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Revisiting Defects4J for Fault Localization in Diverse Development Scenarios
inferred← citeshttps://doi.org/10.1109/q-se66736.2025.00012openalex-snapshot
Quantum Vs. Classical Machine Learning Algorithms for Software Defect Prediction: Challenges and Opportunities
inferred← citeshttps://doi.org/10.1109/saner53432.2022.00012openalex-snapshot
PR-SZZ: How pull requests can support the tracing of defects in software repositories
inferred← citeshttps://doi.org/10.1109/saner60148.2024.00009openalex-snapshot
On the Prevalence, Co-occurrence, and Impact of Infrastructure-as-Code Smells
inferred← citeshttps://doi.org/10.1109/siml69834.2026.11621096openalex-snapshot
A Socio-Technical Analysis of Bug-Introducing Commits in the Apache Ecosystem
inferred← citeshttps://doi.org/10.1109/tce.2025.3614479openalex-snapshot
Identifying Root Cause of Bugs by Capturing Changed Code Lines With Relational Graph Neural Networks
inferred← citeshttps://doi.org/10.1109/tse.2019.2948158openalex-snapshot
Historical Spectrum Based Fault Localization
inferred← citeshttps://doi.org/10.1109/tse.2022.3156787openalex-snapshot
Inconsistent Defect Labels: Essence, Causes, and Influence
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Discovering Reusable Functional Features in Legacy Object-Oriented Systems
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Enhancing Bug-Inducing Commit Identification: A Fine-Grained Semantic Analysis Approach
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Beyond the Sum of Parts: Leveraging Entanglement for Bug Inducing Commit Localization
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Boosting Compiler Fault Localization: Getting the Best of Both Worlds by Fusing Dynamic and Historical Data
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MV-SZZ: An Empirical Study of a Majority Voting-Based SZZ Method
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A code change‐oriented approach to just‐in‐time defect prediction with multiple input semantic fusion
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Where shall we log?
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On the efficiency of test suite based program repair
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Bridging fault localisation and defect prediction
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Boosting automated program repair with bug-inducing commits
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Feature-oriented defect prediction
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OpenSZZ
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Reducing the search space of bug inducing commits using failure coverage
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V-SZZ
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PaReco: patched clones and missed patches among the divergent variants of a software family
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The best of both worlds: integrating semantic features with expert features for defect prediction and localization
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How Useful is Code Change Information for Fault Localization in Continuous Integration?
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An Investigation of Patch Porting Practices of the Linux Kernel Ecosystem
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Understanding Vulnerability Inducing Commits of the Linux Kernel
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Effective Vulnerable Function Identification based on CVE Description Empowered by Large Language Models
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Unveiling the Characteristics and Impact of Security Patch Evolution
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SemBIC: Semantic-Aware Identification of Bug-Inducing Commits
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LLM4SZZ: Enhancing SZZ Algorithm with Context-Enhanced Assessment on Large Language Models
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What Happened in This Pipeline? Diffing Build Logs with CiDiff
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Quantifying SZZ-Induced Evaluation Bias in Just-in-Time Defect Prediction
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Just-in-Time Software Defect Prediction Via Bi-Modal Change Representation Learning
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On the Efficiency of Test Suite based Program Repair: A Systematic Assessment of 16 Automated Repair Systems for Java Programs
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An extensive empirical study of inconsistent labels in multi-version-project defect data sets
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Feature-Oriented Defect Prediction: Scenarios, Metrics, and Classifiers
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