On the public citation graph
Method-level Bug Prediction: Problems and Promises
0 signed · 107 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
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/10.1007/978-1-4614-7138-7openalex-snapshot
An Introduction to Statistical Learning
inferredcites →https://doi.org/10.1007/s10515-015-0179-1openalex-snapshot
Multiple kernel ensemble learning for software defect prediction
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/s10664-016-9437-5openalex-snapshot
Robust Statistical Methods for Empirical Software Engineering
inferredcites →https://doi.org/10.1007/s10664-016-9468-yopenalex-snapshot
Global vs. local models for cross-project defect prediction
inferredcites →https://doi.org/10.1007/s10664-017-9513-5openalex-snapshot
On the correlation between size and metric validity
inferredcites →https://doi.org/10.1007/s10664-017-9545-xopenalex-snapshot
An exploratory study on assessing the energy impact of logging on Android applications
inferredcites →https://doi.org/10.1007/s10664-018-9640-7openalex-snapshot
GreenScaler: training software energy models with automatic test generation
inferredcites →https://doi.org/10.1007/s10664-020-09878-9openalex-snapshot
On the time-based conclusion stability of cross-project defect prediction models
inferredcites →https://doi.org/10.1007/s10664-021-10018-0openalex-snapshot
Maintenance-related concerns for post-deployed Ethereum smart contract development: issues, techniques, and future challenges
inferredcites →https://doi.org/10.1007/s10664-022-10193-8openalex-snapshot
Revisiting the debate: Are code metrics useful for measuring maintenance effort?
inferredcites →https://doi.org/10.1007/s40747-022-00848-wopenalex-snapshot
BugPre: an intelligent software version-to-version bug prediction system using graph convolutional neural networks
inferredcites →https://doi.org/10.1016/j.asoc.2020.106940openalex-snapshot
CFPS: Collaborative filtering based source projects selection for cross-project defect prediction
inferredcites →https://doi.org/10.1016/j.infsof.2011.09.007openalex-snapshot
Transfer learning for cross-company software defect prediction
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.2014.07.005openalex-snapshot
Software defect prediction using ensemble learning on selected features
inferredcites →https://doi.org/10.1016/j.infsof.2014.11.006openalex-snapshot
An empirical study on software defect prediction with a simplified metric set
inferredcites →https://doi.org/10.1016/j.infsof.2017.03.007openalex-snapshot
TLEL: A two-layer ensemble learning approach for just-in-time defect prediction
inferredcites →https://doi.org/10.1016/j.infsof.2021.106794openalex-snapshot
An exploratory study of bug prediction at the method level
inferredcites →https://doi.org/10.1016/j.jss.2009.11.704openalex-snapshot
On the ability of complexity metrics to predict fault-prone classes in object-oriented systems
inferredcites →https://doi.org/10.1016/j.jss.2019.110493openalex-snapshot
On the performance of method-level bug prediction: A negative result
inferredcites →https://doi.org/10.1016/j.jss.2020.110571openalex-snapshot
On tracking Java methods with Git mechanisms
inferredcites →https://doi.org/10.1016/j.procs.2019.11.146openalex-snapshot
Dealing with Noise Problem in Machine Learning Data-sets: A Systematic Review
inferredcites →https://doi.org/10.1023/a:1026582314146openalex-snapshot
On Building Prediction Systems for Software Engineers
inferredcites →https://doi.org/10.1049/iet-sen.2017.0148openalex-snapshot
Progress on approaches to software defect prediction
inferredcites →https://doi.org/10.1080/00107510500052444openalex-snapshot
Power laws, Pareto distributions and Zipf's law
inferredcites →https://doi.org/10.1080/17457300.2020.1746814openalex-snapshot
A machine-learning method for improving crash injury severity analysis: a case study of work zone crashes in Cairo, Egypt
inferredcites →https://doi.org/10.1109/32.295895openalex-snapshot
A metrics suite for object oriented design
inferredcites →https://doi.org/10.1109/32.544352openalex-snapshot
A validation of object-oriented design metrics as quality indicators
inferredcites →https://doi.org/10.1109/32.815326openalex-snapshot
A critique of software defect prediction models
inferredcites →https://doi.org/10.1109/32.859533openalex-snapshot
Predicting fault incidence using software change history
inferredcites →https://doi.org/10.1109/32.935855openalex-snapshot
The confounding effect of class size on the validity of object-oriented metrics
inferredcites →https://doi.org/10.1109/ase.2006.23openalex-snapshot
Automatic Identification of Bug-Introducing Changes
inferredcites →https://doi.org/10.1109/icpc.2017.38openalex-snapshot
An Exploratory Study on the Relationship between Changes and Refactoring
inferredcites →https://doi.org/10.1109/icse.2012.6227193openalex-snapshot
Bug prediction based on fine-grained module histories
inferredcites →https://doi.org/10.1109/icse43902.2021.00049openalex-snapshot
Evaluating SZZ Implementations Through a Developer-Informed Oracle
inferredcites →https://doi.org/10.1109/icsm.1992.242525openalex-snapshot
Metrics for assessing a software system's maintainability
inferredcites →https://doi.org/10.1109/icsm.2000openalex-snapshot
Proceedings 2000 International Conference on Software Maintenance
inferredcites →https://doi.org/10.1109/icsm.2004.1357771openalex-snapshot
Proceedings 20th IEEE International COnference on Software Maintenance
inferredcites →https://doi.org/10.1109/icsme52107.2021.00001openalex-snapshot
Proceedings 2021 IEEE International Conference on Software Maintenance and Evolution
inferredcites →https://doi.org/10.1109/ijcnn.2005.1556327openalex-snapshot
Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2005
inferredcites →https://doi.org/10.1109/ms.2016.156openalex-snapshot
The Tragedy of Defect Prediction, Prince of Empirical Software Engineering Research
inferredcites →https://doi.org/10.1109/quatic.2007.7openalex-snapshot
6th International Conference on the Quality of Information and Communications Technology-TOC
inferredcites →https://doi.org/10.1109/tse.1979.234165openalex-snapshot
Measuring the Psychological Complexity of Software Maintenance Tasks with the Halstead and McCabe Metrics
inferredcites →https://doi.org/10.1109/tse.1987.233164openalex-snapshot
The Use of Software Complexity Metrics in Software Maintenance
inferredcites →https://doi.org/10.1109/tse.2005.112openalex-snapshot
Empirical validation of object-oriented metrics on open source software for fault prediction
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.2009.70openalex-snapshot
Learning a Metric for Code Readability
inferredcites →https://doi.org/10.1109/tse.2010.81openalex-snapshot
Evaluating Complexity, Code Churn, and Developer Activity Metrics as Indicators of Software Vulnerabilities
inferredcites →https://doi.org/10.1109/tse.2010.90openalex-snapshot
A General Software Defect-Proneness Prediction Framework
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.2014.2367027openalex-snapshot
The Impact of API Change- and Fault-Proneness on the User Ratings of Android Apps
inferredcites →https://doi.org/10.1109/tse.2017.2659747openalex-snapshot
A Developer Centered Bug Prediction Model
inferredcites →https://doi.org/10.1109/tse.2017.2770124openalex-snapshot
A Systematic Literature Review and Meta-Analysis on Cross Project Defect Prediction
inferredcites →https://doi.org/10.1109/tse.2018.2877678openalex-snapshot
Perceptions, Expectations, and Challenges in Defect Prediction
inferredcites →https://doi.org/10.1109/tse.2020.3023177openalex-snapshot
Predicting Defective Lines Using a Model-Agnostic Technique
inferredcites →https://doi.org/10.1109/tsmcc.2012.2226152openalex-snapshot
Using Coding-Based Ensemble Learning to Improve Software Defect Prediction
inferredcites →https://doi.org/10.11144/javeriana.upsy10-2.cdcpopenalex-snapshot
Cliff´s Delta Calculator: A non-parametric effect size program for two groups of observations
inferredcites →https://doi.org/10.1145/1062455.1062514openalex-snapshot
Use of relative code churn measures to predict system defect density
inferredcites →https://doi.org/10.1145/1082983.1083147openalex-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/10.1145/1390156openalex-snapshot
Proceedings of the 25th international conference on Machine learning - ICML '08
inferredcites →https://doi.org/10.1145/1414004.1414063openalex-snapshot
Analysis of the reliability of a subset of change metrics for defect prediction
inferredcites →https://doi.org/10.1145/2020390.2020392openalex-snapshot
Does measuring code change improve fault prediction?
inferredcites →https://doi.org/10.1145/2393596.2393670openalex-snapshot
An industrial study on the risk of software changes
inferredcites →https://doi.org/10.1145/2557833.2557853openalex-snapshot
Cyclomatic complexity metric for component based software
inferredcites →https://doi.org/10.1145/2597008.2597798openalex-snapshot
Hey! are you committing tangled changes?
inferredcites →https://doi.org/10.1145/2597073.2597111openalex-snapshot
Incremental origin analysis of source code files
inferredcites →https://doi.org/10.1145/2884781.2884848openalex-snapshot
On the "naturalness" of buggy code
inferredcites →https://doi.org/10.1145/2961111.2962620openalex-snapshot
So You Need More Method Level Datasets for Your Software Defect Prediction?
inferredcites →https://doi.org/10.1145/3183519.3183547openalex-snapshot
An experience report on defect modelling in practice
inferredcites →https://doi.org/10.1145/3210459openalex-snapshot
Proceedings of the 22nd International Conference on Evaluation and Assessment in Software Engineering 2018
inferredcites →https://doi.org/10.1145/3475716.3475790openalex-snapshot
Continuous Software Bug Prediction
inferredcites →https://doi.org/10.1145/3524842.3527975openalex-snapshot
An empirical study on maintainable method size in Java
inferredcites →https://doi.org/10.14288/1.0401968openalex-snapshot
Investigating the impact of methodological choices on source code maintenance analyses
inferredcites →https://doi.org/10.5381/jot.2016.15.1.a2openalex-snapshot
When do Software Complexity Metrics Mean Nothing? – When Examined out of Context.
inferredcites →https://doi.org/10.6084/m9.figshare.9980672openalex-snapshot
Testing of Mobile Applications in the Wild: A Large-Scale Empirical Study on Android Apps
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4805882openalex-extracted
Impact of Methodological Choices on the Analysis of Code Metrics And Maintenance
inferred← citeshttps://doi.org/10.1007/s10664-025-10674-6openalex-snapshot
Enhancing line-level defect prediction using bilinear attention fusion and ranking optimization
inferred← citeshttps://doi.org/10.1016/j.cola.2026.101402openalex-snapshot
Empirical analysis of code smell prediction in mobile applications based on software quality indicators
inferred← citeshttps://doi.org/10.1016/j.jss.2024.112179openalex-snapshot
An empirical study on bug severity estimation using source code metrics and static analysis
inferred← citeshttps://doi.org/10.1016/j.jss.2024.112263openalex-snapshot
Impact of methodological choices on the analysis of code metrics and maintenance
inferred← citeshttps://doi.org/10.1016/j.jss.2025.112342openalex-snapshot
Best practices for evaluating IRFL approaches
inferred← citeshttps://doi.org/10.1016/j.jss.2026.113120openalex-snapshot
A first look at the self-admitted technical debt in test code: Taxonomy and detection
inferred← citeshttps://doi.org/10.1109/access.2024.3406929openalex-snapshot
The Effectiveness of Hidden Dependence Metrics in Bug Prediction
inferred← citeshttps://doi.org/10.1109/aibthings66987.2025.11296163openalex-snapshot
Integrating LLMs for Automated Bug Triaging and Root Cause Localization in Software Systems
inferred← citeshttps://doi.org/10.1109/blockchain62396.2024.00075openalex-snapshot
Versioned Analysis of Software Quality Indicators and Self-admitted Technical Debt in Ethereum Smart Contracts with Ethstractor
inferred← citeshttps://doi.org/10.1109/icccnt61001.2024.10724583openalex-snapshot
Comparison of Novel Random Forest Algorithm over Gaussian Naïve Bayes for Improving Accuracy in Software Bug Prediction System
inferred← citeshttps://doi.org/10.1109/icses63760.2024.10910397openalex-snapshot
Designing a Robust Software Bug Prediction Model Using Enhanced Learning Principles with Artificial Intelligence Assistance
inferred← citeshttps://doi.org/10.1109/msr66628.2025.00118openalex-snapshot
Evidence is All We Need: Do Self-Admitted Technical Debts Impact Method-Level Maintenance?
inferred← citeshttps://doi.org/10.1109/qrs65678.2025.00056openalex-snapshot
CAFEM: Context-Aware Feature Enhancement with Multi-Stage Sampling for Statement-Level Defect Prediction
inferred← citeshttps://doi.org/10.1145/3715006openalex-snapshot
The Good, the Bad, and the Monstrous: Predicting Highly Change-Prone Source Code Methods at Their Inception
inferred← citeshttps://doi.org/10.1145/3793302.3793351openalex-snapshot
LLM-Based Detection of Tangled Code Changes for Higher-Quality Method-Level Bug Datasets
inferred← citeshttps://doi.org/10.2139/ssrn.5963911openalex-snapshot
A First Look at the Self-Admitted Technical Debt in Test Code: Taxonomy and Detection
inferred← citeshttps://doi.org/10.2139/ssrn.6479525openalex-snapshot
A First Look at the Self-Admitted Technical Debt in Test Code: Taxonomy and Detection
inferred← citeshttps://doi.org/10.22271/maths.2025.v10.i4a.2018openalex-snapshot
An analysis of code quality metrics applying correlation and regression for identifying bug occurrences
inferred← citeshttps://doi.org/10.3390/electronics15102095openalex-snapshot
On the Use of an Extreme Learning Machine for GitHub Repository Popularity Prediction Based on Static Software Metrics
inferred← citeshttps://doi.org/10.4236/jamp.2024.125115openalex-snapshot
Objective Model Selection in Physics: Exploring the Finite Information Quantity Approach
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.