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Code-line-level Bugginess Identification: How Far have We Come, and How Far have We Yet to Go?
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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A Survival Analysis-Based Prioritization of Code Checker Warning: A Case Study Using PMD
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Defect prediction from static code features: current results, limitations, new approaches
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Studying just-in-time defect prediction using cross-project models
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ChangeLocator: locate crash-inducing changes based on crash reports
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The impact of context metrics on just-in-time defect prediction
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How developers engage with static analysis tools in different contexts
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The impact of automated feature selection techniques on the interpretation of defect models
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A longitudinal study of static analysis warning evolution and the effects of PMD on software quality in Apache open source projects
inferredcites →https://doi.org/10.1007/s10664-021-10083-5openalex-snapshot
A fine-grained data set and analysis of tangling in bug fixing commits
inferredcites →https://doi.org/10.1016/j.asoc.2015.04.045openalex-snapshot
Software defect prediction using cost-sensitive neural network
inferredcites →https://doi.org/10.1016/j.csi.2017.02.003openalex-snapshot
An empirical analysis of the effectiveness of software metrics and fault prediction model for identifying faulty classes
inferredcites →https://doi.org/10.1016/j.eswa.2019.113156openalex-snapshot
SLDeep: Statement-level software defect prediction using deep-learning model on static code features
inferredcites →https://doi.org/10.1016/j.infsof.2013.02.009openalex-snapshot
Software fault prediction metrics: A systematic literature review
inferredcites →https://doi.org/10.1016/j.infsof.2014.11.006openalex-snapshot
An empirical study on software defect prediction with a simplified metric set
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On the performance of method-level bug prediction: A negative result
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A validation of object-oriented design metrics as quality indicators
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Quantitative analysis of faults and failures in a complex software system
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Automatic Classifying Self-Admitted Technical Debt Using N-Gram IDF
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Alattin: Mining Alternative Patterns for Detecting Neglected Conditions
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File-Level Defect Prediction: Unsupervised vs. Supervised Models
inferredcites →https://doi.org/10.1109/esem.2017.8openalex-snapshot
Code Churn: A Neglected Metric in Effort-Aware Just-in-Time Defect Prediction
inferredcites →https://doi.org/10.1109/icse.2007.66openalex-snapshot
Predicting Faults from Cached History
inferredcites →https://doi.org/10.1109/icse.2009.5070510openalex-snapshot
Predicting faults using the complexity of code changes
inferredcites →https://doi.org/10.1109/icse.2012.6227135openalex-snapshot
On the naturalness of software
inferredcites →https://doi.org/10.1109/icse.2012.6227210openalex-snapshot
Where should the bugs be fixed? More accurate information retrieval-based bug localization based on bug reports
inferredcites →https://doi.org/10.1109/icse.2013.6606613openalex-snapshot
Why don't software developers use static analysis tools to find bugs?
inferredcites →https://doi.org/10.1109/icse.2019.00075openalex-snapshot
Mining Software Defects: Should We Consider Affected Releases?
inferredcites →https://doi.org/10.1109/icsm.2010.5609530openalex-snapshot
Revisiting common bug prediction findings using effort-aware models
inferredcites →https://doi.org/10.1109/icsme.2014.40openalex-snapshot
Boosting Bug-Report-Oriented Fault Localization with Segmentation and Stack-Trace Analysis
inferredcites →https://doi.org/10.1109/icsme46990.2020.00022openalex-snapshot
Static source code metrics and static analysis warnings for fine-grained just-in-time defect prediction
inferredcites →https://doi.org/10.1109/issre.2019.00040openalex-snapshot
How Do Developers Act on Static Analysis Alerts? An Empirical Study of Coverity Usage
inferredcites →https://doi.org/10.1109/msr.2017.2openalex-snapshot
How Open Source Projects Use Static Code Analysis Tools in Continuous Integration Pipelines
inferredcites →https://doi.org/10.1109/msr.2019.00016openalex-snapshot
DeepJIT: An End-to-End Deep Learning Framework for Just-in-Time Defect Prediction
inferredcites →https://doi.org/10.1109/msr.2019.00049openalex-snapshot
Challenges with Responding to Static Analysis Tool Alerts
inferredcites →https://doi.org/10.1109/msr52588.2021.00049openalex-snapshot
JITLine: A Simpler, Better, Faster, Finer-grained Just-In-Time Defect Prediction
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/saner.2016.105openalex-snapshot
Analyzing the State of Static Analysis: A Large-Scale Evaluation in Open Source Software
inferredcites →https://doi.org/10.1109/saner.2018.8330264openalex-snapshot
Re-evaluating method-level bug prediction
inferredcites →https://doi.org/10.1109/tse.2005.49openalex-snapshot
Predicting the location and number of faults in large software systems
inferredcites →https://doi.org/10.1109/tse.2007.256941openalex-snapshot
Data Mining Static Code Attributes to Learn Defect Predictors
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.2013.21openalex-snapshot
Assessing the Cost Effectiveness of Fault Prediction in Acceptance Testing
inferredcites →https://doi.org/10.1109/tse.2017.2693980openalex-snapshot
Are Fix-Inducing Changes a Moving Target? A Longitudinal Case Study of Just-In-Time Defect Prediction
inferredcites →https://doi.org/10.1109/tse.2018.2790925openalex-snapshot
“Sampling” as a Baseline Optimizer for Search-Based Software Engineering
inferredcites →https://doi.org/10.1109/tse.2019.2945020openalex-snapshot
How to “DODGE” Complex Software Analytics
inferredcites →https://doi.org/10.1109/tse.2020.2968072openalex-snapshot
RefDiff 2.0: A Multi-Language Refactoring Detection Tool
inferredcites →https://doi.org/10.1109/tse.2020.2978819openalex-snapshot
Just-In-Time Defect Identification and Localization: A Two-Phase Framework
inferredcites →https://doi.org/10.1109/tse.2020.2982385openalex-snapshot
An Empirical Study of Model-Agnostic Techniques for Defect Prediction Models
inferredcites →https://doi.org/10.1109/tse.2020.3023177openalex-snapshot
Predicting Defective Lines Using a Model-Agnostic Technique
inferredcites →https://doi.org/10.1109/tse.2020.3031401openalex-snapshot
Identifying Self-Admitted Technical Debts With Jitterbug: A Two-Step Approach
inferredcites →https://doi.org/10.1111/j.2517-6161.1995.tb02031.xopenalex-snapshot
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
inferredcites →https://doi.org/10.1145/1082983.1083172openalex-snapshot
An investigation of the effect of module size on defect prediction using static measures
inferredcites →https://doi.org/10.1145/1083142.1083147openalex-snapshot
When do changes induce fixes?
inferredcites →https://doi.org/10.1145/1134285.1134349openalex-snapshot
Mining metrics to predict component failures
inferredcites →https://doi.org/10.1145/1251535.1251537openalex-snapshot
Finding more null pointer bugs, but not too many
inferredcites →https://doi.org/10.1145/1287624.1287632openalex-snapshot
Detecting object usage anomalies
inferredcites →https://doi.org/10.1145/1287624.1287633openalex-snapshot
Which warnings should I fix first?
inferredcites →https://doi.org/10.1145/1368088.1368160openalex-snapshot
The influence of organizational structure on software quality
inferredcites →https://doi.org/10.1145/2001420.2001445openalex-snapshot
Are automated debugging techniques actually helping programmers?
inferredcites →https://doi.org/10.1145/2491411.2494581openalex-snapshot
A cost-effectiveness criterion for applying software defect prediction models
inferredcites →https://doi.org/10.1145/2568225.2568269openalex-snapshot
Comparing static bug finders and statistical prediction
inferredcites →https://doi.org/10.1145/2594291.2594321openalex-snapshot
Code completion with statistical language models
inferredcites →https://doi.org/10.1145/2597073.2597102openalex-snapshot
Syntax errors just aren't natural: improving error reporting with language models
inferredcites →https://doi.org/10.1145/2610384.2610386openalex-snapshot
CrashLocator: locating crashing faults based on crash stacks
inferredcites →https://doi.org/10.1145/2884781.2884848openalex-snapshot
On the "naturalness" of buggy code
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/10.1145/2970276.2970341openalex-snapshot
Bugram: bug detection with n-gram language models
inferredcites →https://doi.org/10.1145/2970276.2970353openalex-snapshot
An empirical study on dependence clusters for effort-aware fault-proneness prediction
inferredcites →https://doi.org/10.1145/3106237.3106256openalex-snapshot
Easy over hard: a case study on deep learning
inferredcites →https://doi.org/10.1145/3106237.3106290openalex-snapshot
Are deep neural networks the best choice for modeling source code?
inferredcites →https://doi.org/10.1145/3183339openalex-snapshot
How Far We Have Progressed in the Journey? An Examination of Cross-Project Defect Prediction
inferredcites →https://doi.org/10.1145/3196398.3196424openalex-snapshot
500+ times faster than deep learning
inferredcites →https://doi.org/10.1145/3213846.3213856openalex-snapshot
Bench4BL: reproducibility study on the performance of IR-based bug localization
inferredcites →https://doi.org/10.1145/3236024.3236032openalex-snapshot
NAR-miner: discovering negative association rules from code for bug detection
inferredcites →https://doi.org/10.1145/3238147.3238190openalex-snapshot
Neural-machine-translation-based commit message generation: how far are we?
inferredcites →https://doi.org/10.1145/3238147.3238213openalex-snapshot
How many of all bugs do we find? a study of static bug detectors
inferredcites →https://doi.org/10.1145/3447247openalex-snapshot
How Far Have We Progressed in Identifying Self-admitted Technical Debts? A Comprehensive Empirical Study
inferredcites →https://doi.org/10.1145/3540250.3549165openalex-snapshot
The best of both worlds: integrating semantic features with expert features for defect prediction and localization
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/smr.2696openalex-snapshot
Deep learning or classical machine learning? An empirical study on line‐level software defect prediction
inferred← citeshttps://doi.org/10.1007/s00521-024-09930-5openalex-snapshot
Parameter-efficient fine-tuning of pre-trained code models for just-in-time defect prediction
inferred← citeshttps://doi.org/10.1007/s10515-024-00424-1openalex-snapshot
Software defect prediction: future directions and challenges
inferred← citeshttps://doi.org/10.1007/s10664-026-10855-xopenalex-snapshot
PRaFFLineDP: Feature fusion with progressive ranking for efficient line-level defect prediction
inferred← citeshttps://doi.org/10.1007/s10664-026-10895-3openalex-snapshot
Line-level bug-finding power of static analysis rules: a case study of Teamscale
inferred← citeshttps://doi.org/10.1016/j.neucom.2026.134133openalex-snapshot
Neural network-driven code semantic alignment for software engineering: Three-perspective framework, three-dimensional evaluation, and industrial adaptation
inferred← citeshttps://doi.org/10.1109/icse55347.2025.00240openalex-snapshot
Boosting Code-line-level Defect Prediction with Spectrum Information and Causality Analysis
inferred← citeshttps://doi.org/10.1109/tse.2024.3503723openalex-snapshot
Line-Level Defect Prediction by Capturing Code Contexts With Graph Convolutional Networks
inferred← citeshttps://doi.org/10.1109/tse.2026.3704180openalex-snapshot
Boosting High-Review-Value Warning Line Identification With Unsupervised Line-Level Defect Prediction
inferred← citeshttps://doi.org/10.1142/s0218194024500050openalex-snapshot
Multi-Intent Inline Code Comment Generation via Large Language Model
inferred← citeshttps://doi.org/10.1145/3643916.3644414openalex-snapshot
ESGen: Commit Message Generation Based on Edit Sequence of Code Change
inferred← citeshttps://doi.org/10.1145/3643916.3644428openalex-snapshot
A Just-in-time Software Defect Localization Method based on Code Graph Representation
inferred← citeshttps://doi.org/10.1145/3707456openalex-snapshot
Refining Code-Line-Level Bugginess Identification: Getting the Best of Both Worlds by Fusing Syntactic and Semantic Features
inferred← citeshttps://doi.org/10.1145/3746225openalex-snapshot
Estimating Uncertainty in Line-Level Defect Prediction via Perceptual Borderline Oversampling
inferred← citeshttps://doi.org/10.1145/3772717openalex-snapshot
ByteCue: Enhancing Bytecode Comment Generation with API Information
inferred← citeshttps://doi.org/10.1145/3793302.3793359openalex-snapshot
An Empirical Study on Deep Learning-based Line-Level Software Defect Prediction
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