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A study of subgroup discovery approaches for defect prediction
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Finding software metrics threshold values using ROC curves
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An algorithm for multi-relational discovery of subgroups
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Defect prediction from static code features: current results, limitations, new approaches
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Evaluating defect prediction approaches: a benchmark and an extensive comparison
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Empirical validation of object-oriented metrics for predicting fault proneness models
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A Practical Approach to Feature Selection
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Data Mining: Practical Machine Learning Tools and Techniques
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A systematic review of software fault prediction studies
inferredcites →https://doi.org/10.1016/j.infsof.2009.05.003openalex-snapshot
A hybrid heuristic approach to optimize rule-based software quality estimation models
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An ant colony optimization algorithm to improve software quality prediction models: Case of class stability
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Software fault prediction metrics: A systematic literature review
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User preferences based software defect detection algorithms selection using MCDM
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Searching for rules to detect defective modules: A subgroup discovery approach
inferredcites →https://doi.org/10.1016/j.jss.2007.07.034openalex-snapshot
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Predicting defect-prone software modules using support vector machines
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A systematic and comprehensive investigation of methods to build and evaluate fault prediction models
inferredcites →https://doi.org/10.1016/j.patrec.2005.10.010openalex-snapshot
An introduction to ROC analysis
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Experimental assessment of the effect of inheritance on the maintainability of object-oriented systems
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Experimentation in software engineering: An introduction
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Analogy-Based Practical Classification Rules for Software Quality Estimation
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Comparative Assessment of Software Quality Classification Techniques: An Empirical Case Study
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Reformulating software engineering as a search problem
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A metrics suite for object oriented design
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A critique of software defect prediction models
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Application of neural networks to software quality modeling of a very large telecommunications system
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Software Defect Prediction Using Regression via Classification
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Effort-Aware Defect Prediction Models
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Detecting Fault Modules Applying Feature Selection to Classifiers
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Thresholds for object-oriented measures
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Data Mining for Software Engineering
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Building Defect Prediction Models in Practice
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An extensive comparison of bug prediction approaches
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Predicting Defects for Eclipse
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Empirical validation of object-oriented metrics on open source software for fault prediction
inferredcites →https://doi.org/10.1109/tse.2005.58openalex-snapshot
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Software defect association mining and defect correction effort prediction
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Data Mining Static Code Attributes to Learn Defect Predictors
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Comments on "Data Mining Static Code Attributes to Learn Defect Predictors"
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Problems with Precision: A Response to "Comments on 'Data Mining Static Code Attributes to Learn Defect Predictors'"
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Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings
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A Systematic Literature Review on Fault Prediction Performance in Software Engineering
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Local versus Global Lessons for Defect Prediction and Effort Estimation
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EMPIRICAL EVALUATION OF CLASSIFIERS FOR SOFTWARE RISK MANAGEMENT
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Design complexity measurement and testing
inferredcites →https://doi.org/10.1613/jair.1089openalex-snapshot
Expert-Guided Subgroup Discovery: Methodology and Application
inferredcites →https://doi.org/10.3233/ida-2010-0475openalex-snapshot
Data mining in software engineering
inferredcites →https://doi.org/10.5555/1005332.1005338openalex-snapshot
Subgroup Discovery with CN2-SD
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Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-319-07593-8_21openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-319-10389-1_13openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-981-15-6067-5_62openalex-snapshot
Software Quality Prediction Using Machine Learning Techniques
inferred← citeshttps://doi.org/10.1007/978-981-99-6550-2_38openalex-snapshot
Analysis and Study of Bug Classification Quintessence and Techniques for Forecasting Software Faults
inferred← citeshttps://doi.org/10.1007/s10270-023-01090-9openalex-snapshot
MoDALAS: addressing assurance for learning-enabled autonomous systems in the face of uncertainty
inferred← citeshttps://doi.org/10.1007/s11042-021-11441-7openalex-snapshot
EkmEx - an extended framework for labeling an unlabeled fault dataset
inferred← citeshttps://doi.org/10.1007/s13198-016-0479-2openalex-snapshot
An empirical study of software entropy based bug prediction using machine learning
inferred← citeshttps://doi.org/10.1007/s13369-017-2653-5openalex-snapshot
Evolutionary Computation-Based Techniques Over Multiple Data Sets: An Empirical Assessment
inferred← citeshttps://doi.org/10.1007/s41060-023-00440-6openalex-snapshot
Hyperparameter analysis of wide-kernel CNN architectures in industrial fault detection: an exploratory study
inferred← citeshttps://doi.org/10.1016/j.asoc.2016.08.025openalex-snapshot
Iterative software fault prediction with a hybrid approach
inferred← citeshttps://doi.org/10.1016/j.compeleceng.2018.02.043openalex-snapshot
Empirical analysis of change metrics for software fault prediction
inferred← citeshttps://doi.org/10.1016/j.engappai.2022.104773openalex-snapshot
A systematic literature review on software defect prediction using artificial intelligence: Datasets, Data Validation Methods, Approaches, and Tools
inferred← citeshttps://doi.org/10.1016/j.eswa.2014.10.025openalex-snapshot
A comparison of some soft computing methods for software fault prediction
inferred← citeshttps://doi.org/10.1016/j.infsof.2014.06.015openalex-snapshot
Investigating software testing and maintenance reports: Case study
inferred← citeshttps://doi.org/10.1016/j.infsof.2017.03.007openalex-snapshot
TLEL: A two-layer ensemble learning approach for just-in-time defect prediction
inferred← citeshttps://doi.org/10.1016/j.infsof.2017.07.004openalex-snapshot
Which type of metrics are useful to deal with class imbalance in software defect prediction?
inferred← citeshttps://doi.org/10.1016/j.jnlest.2020.100029openalex-snapshot
Machine learning application for prediction of sapphire crystals defects
inferred← citeshttps://doi.org/10.1016/j.neucom.2021.05.043openalex-snapshot
Analysis and modeling conditional mutual dependency of metrics in software defect prediction using latent variables
inferred← citeshttps://doi.org/10.1109/icacci.2014.6968592openalex-snapshot
On the applicability of evolutionary computation for software defect prediction
inferred← citeshttps://doi.org/10.1109/iciibms60103.2023.10347776openalex-snapshot
A Just-In-Time Software Prediction Model Based On A Double-Weighted Adaboost
inferred← citeshttps://doi.org/10.1109/icimia60377.2023.10426557openalex-snapshot
An End-to-End S-AF-RCNN based Framework for Just-in-Time Defect Prediction
inferred← citeshttps://doi.org/10.1109/icirca.2018.8597382openalex-snapshot
Review on Code Examination Proficient System in Software Engineering by Using Machine Learning Approach
inferred← citeshttps://doi.org/10.1109/icsess.2014.6933517openalex-snapshot
Analyzing the significance of process metrics for TT&C software defect prediction
inferred← citeshttps://doi.org/10.1109/inista.2018.8466331openalex-snapshot
Verification of the effectiveness of fuzzy rule-based fault prediction: A replication study
inferred← citeshttps://doi.org/10.1109/models50736.2021.00027openalex-snapshot
MoDALAS: Model-Driven Assurance for Learning-Enabled Autonomous Systems
inferred← citeshttps://doi.org/10.1109/sose.2016.25openalex-snapshot
Mobile Application Software Defect Prediction
inferred← citeshttps://doi.org/10.1145/3468264.3468614openalex-snapshot
Explaining mispredictions of machine learning models using rule induction
inferred← citeshttps://doi.org/10.32614/rj-2016-048openalex-snapshot
Subgroup Discovery with Evolutionary Fuzzy Systems in R: The SDEFSR Package
inferred← citeshttps://doi.org/10.33166/aetic.2024.01.004openalex-snapshot
Explainable Software Defects Classification Using SMOTE and Machine Learning
inferred← citeshttps://doi.org/10.3390/app112311377openalex-snapshot
Techniques for Calculating Software Product Metrics Threshold Values: A Systematic Mapping Study
inferred← citeshttps://doi.org/10.3390/sym11020212openalex-snapshot
Empirical Study of Software Defect Prediction: A Systematic Mapping
inferred← citeshttps://doi.org/10.4018/ijkss.2020040102openalex-snapshot
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inferred← citeshttps://doi.org/10.4018/ijossp.287611openalex-snapshot
A Software Fault Prediction on Inter- and Intra-Release Prediction Scenarios
inferred← citeshttps://doi.org/10.4018/ijsi.2014100105openalex-snapshot
Increasing the Accuracy of Software Fault Prediction using Majority Ranking Fuzzy Clustering
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