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The Tragedy of Defect Prediction, Prince of Empirical Software Engineering Research

https://doi.org/10.1109/ms.2016.156

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What this work says

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

inferredcites →https://doi.org/10.1007/s10664-011-9173-9openalex-snapshot

Evaluating defect prediction approaches: a benchmark and an extensive comparison

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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

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/tse.1985.232222openalex-snapshot

Identifying Error-Prone Software—An Empirical Study

inferredcites →https://doi.org/10.1109/tse.2003.1191795openalex-snapshot

Empirical analysis of CK metrics for object-oriented design complexity: implications for software defects

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/10.1145/1062455.1062514openalex-snapshot

Use of relative code churn measures to predict system defect density

inferredcites →https://doi.org/10.1145/1342211.1342216openalex-snapshot

Predicting faults from cached history

What points at this work

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

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Search-Based Predictive Modelling for Software Engineering: How Far Have We Gone?

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Complexity of the Code Changes and Issues Dependent Approach to Determine the Release Time of Software Product

inferred← citeshttps://doi.org/10.1007/s10489-021-02346-xopenalex-snapshot

Software fault prediction based on the dynamic selection of learning technique: findings from the eclipse project study

inferred← citeshttps://doi.org/10.1007/s10489-023-04523-6openalex-snapshot

Continuous build outcome prediction: an experimental evaluation and acceptance modelling

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The who, what, how of software engineering research: a socio-technical framework

inferred← citeshttps://doi.org/10.1007/s11831-022-09787-8openalex-snapshot

A Survey of Software Defect Prediction Based on Deep Learning

inferred← citeshttps://doi.org/10.1016/j.eswa.2019.113156openalex-snapshot

SLDeep: Statement-level software defect prediction using deep-learning model on static code features

inferred← citeshttps://doi.org/10.1016/j.infsof.2018.02.003openalex-snapshot

Reproducibility and replicability of software defect prediction studies

inferred← citeshttps://doi.org/10.1016/j.infsof.2022.107128openalex-snapshot

Machine learning in software defect prediction: A business-driven systematic mapping study

inferred← citeshttps://doi.org/10.1016/j.infsof.2023.107192openalex-snapshot

Industrial applications of software defect prediction using machine learning: A business-driven systematic literature review

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On the performance of method-level bug prediction: A negative result

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Adaptive selection of classifiers for bug prediction: A large-scale empirical analysis of its performances and a benchmark study

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Bridging the Gap Between Academia and Industry in Machine Learning Software Defect Prediction: Thirteen Considerations

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Can we Knapsack Software Defect Prediction? Nokia 5G Case

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Methods and Tools for Focusing and Prioritizing the Testing Effort

inferred← citeshttps://doi.org/10.1109/issrew.2017.11openalex-snapshot

Getting Defect Prediction Into Industrial Practice: the ELFF Tool

inferred← citeshttps://doi.org/10.1109/ms.2024.3505544openalex-snapshot

Interpretability/Explainability Applied to Machine Learning Software Defect Prediction: An Industrial Perspective

inferred← citeshttps://doi.org/10.1109/saner.2018.8330264openalex-snapshot

Re-evaluating method-level bug prediction

inferred← citeshttps://doi.org/10.1109/scam59687.2023.00039openalex-snapshot

Uncovering the Hidden Risks: The Importance of Predicting Bugginess in Untouched Methods

inferred← citeshttps://doi.org/10.1109/tse.2017.2770124openalex-snapshot

A Systematic Literature Review and Meta-Analysis on Cross Project Defect Prediction

inferred← citeshttps://doi.org/10.1109/tse.2018.2877678openalex-snapshot

Perceptions, Expectations, and Challenges in Defect Prediction

inferred← citeshttps://doi.org/10.1145/3180155.3180212openalex-snapshot

Assessing the threat of untracked changes in software evolution

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How Far We Have Progressed in the Journey? An Examination of Cross-Project Defect Prediction

inferred← citeshttps://doi.org/10.1145/3236024.3236054openalex-snapshot

Be careful of when: an empirical study on time-related misuse of issue tracking data

inferred← citeshttps://doi.org/10.1145/3640331openalex-snapshot

Method-level Bug Prediction: Problems and Promises

inferred← citeshttps://doi.org/10.1145/3663529.3663831openalex-snapshot

Costs and Benefits of Machine Learning Software Defect Prediction: Industrial Case Study

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/10.1371/journal.pone.0247444openalex-snapshot

Attention based GRU-LSTM for software defect prediction

inferred← citeshttps://doi.org/10.14569/ijacsa.2018.090857openalex-snapshot

Defects Prediction and Prevention Approaches for Quality Software Development

inferred← citeshttps://doi.org/10.20965/jaciii.2019.p0340openalex-snapshot

Efficient Prediction Method of Defect of Monitor Configuration Software

inferred← citeshttps://doi.org/10.3844/jcssp.2020.1709.1717openalex-snapshot

Artificial Intelligence for Software Engineering: An Initial Review on Software Bug Detection and Prediction

inferred← citeshttps://doi.org/10.46632/rmc/1/3/13openalex-snapshot

Software Engineering Defect Prediction using the SPSS Method

inferred← citeshttps://doi.org/10.48550/arxiv.1801.07194openalex-snapshot

Optimizing Prediction Intervals by Tuning Random Forest via Meta-Validation

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Predicting post-release software faults in open source software as a means of measuring intrinsic software product quality

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