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Better cross company defect prediction

https://doi.org/10.1109/msr.2013.6624057

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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/bfb0026666openalex-snapshot

Naive (Bayes) at forty: The independence assumption in information retrieval

inferredcites →https://doi.org/10.1007/s10515-011-0090-3openalex-snapshot

An investigation on the feasibility of cross-project defect prediction

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Guest editorial: learning to organize testing

inferredcites →https://doi.org/10.1007/s10664-008-9079-3openalex-snapshot

Techniques for evaluating fault prediction models

inferredcites →https://doi.org/10.1007/s10664-008-9082-8openalex-snapshot

Do too many cooks spoil the broth? Using the number of developers to enhance defect prediction models

inferredcites →https://doi.org/10.1007/s10664-008-9103-7openalex-snapshot

On the relative value of cross-company and within-company data for 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.patrec.2009.09.011openalex-snapshot

Data clustering: 50 years beyond K-means

inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot

Random Forests

inferredcites →https://doi.org/10.1109/32.6191openalex-snapshot

Understanding and controlling software costs

inferredcites →https://doi.org/10.1109/apsec.2010.54openalex-snapshot

Using Faults-Slip-Through Metric as a Predictor of Fault-Proneness

inferredcites →https://doi.org/10.1109/ase.2011.6100072openalex-snapshot

Local vs. global models for effort estimation and defect prediction

inferredcites →https://doi.org/10.1109/esem.2011.34openalex-snapshot

How to Find Relevant Data for Effort Estimation?

inferredcites →https://doi.org/10.1109/metric.2002.1011343openalex-snapshot

What we have learned about fighting defects

inferredcites →https://doi.org/10.1109/raise.2012.6227971openalex-snapshot

On software engineering repositories and their open problems

inferredcites →https://doi.org/10.1109/tit.1967.1053964openalex-snapshot

Nearest neighbor pattern classification

inferredcites →https://doi.org/10.1109/tse.2007.1001openalex-snapshot

Cross versus Within-Company Cost Estimation Studies: A Systematic Review

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.2007.70721openalex-snapshot

Problems with Precision: A Response to "Comments on 'Data Mining Static Code Attributes to Learn Defect Predictors'"

inferredcites →https://doi.org/10.1109/tse.2008.35openalex-snapshot

Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings

inferredcites →https://doi.org/10.1109/tse.2013.6openalex-snapshot

Balancing Privacy and Utility in Cross-Company Defect Prediction

inferredcites →https://doi.org/10.1145/1540438.1540453openalex-snapshot

Practical considerations in deploying AI for defect prediction

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The WEKA data mining software

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Web-scale k-means clustering

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When to use data from other projects for effort estimation

inferredcites →https://doi.org/10.1145/1868328.1868342openalex-snapshot

Towards identifying software project clusters with regard to defect prediction

inferredcites →https://doi.org/10.1145/2393596.2393669openalex-snapshot

Recalling the "imprecision" of cross-project defect prediction

inferredcites →https://doi.org/10.1145/347090.347123openalex-snapshot

Efficient clustering of high-dimensional data sets with application to reference matching

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AI‐Based Software Defect Predictors: Applications and Benefits in a Case Study

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Cross-project defect prediction: a large scale experiment on data vs. domain vs. process

What points at this work

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

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Daanae: A Novel Approach to Cross-Project Defect Prediction Based on Dynamic Adversarial Adaptation Network and Autoencoder

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Cross‐project defect prediction method based on genetic algorithm feature selection

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An improved transfer adaptive boosting approach for mixed‐project defect prediction

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Multitask defect prediction

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Do different cross‐project defect prediction methods identify the same defective modules?

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Heterogeneous fault prediction with cost‐sensitive domain adaptation

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CSSG: A cost‐sensitive stacked generalization approach for software defect prediction

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Cross-Project Issue Classification Based on Ensemble Modeling in a Social Coding World

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Feature Representation and Feature Matching for Heterogeneous Defect Prediction

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Cross Projects Defect Prediction Modeling

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A Three-Level Training Data Filter for Cross-project Defect Prediction

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Validation-Controlled Model Selection and Soft Ensembling for Imbalanced Software Defect Prediction: A Repeated Within- and Cross-Project Evaluation

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Developing Prediction Models to Assist Software Developers and Support Managers

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Analytical Approach to Cross Project Defect Prediction

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Bug Priority Assessment in Cross-Project Context Using Entropy-Based Measure

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Heterogeneous Defect Prediction

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An Empirical Study on HDP Approaches

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Cross-company defect prediction via semi-supervised clustering-based data filtering and MSTrA-based transfer learning

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An empirical evaluation of defect prediction approaches in within-project and cross-project context

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A Cluster Based Feature Selection Method for Cross-Project Software Defect Prediction

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Cross Project Defect Prediction via Balanced Distribution Adaptation Based Transfer Learning

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Effort-aware cross-project just-in-time defect prediction framework for mobile apps

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Predicting Fault-Proneness of Reused Object-Oriented Classes in Software Post-Releases

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An Improved Method for Training Data Selection for Cross-Project Defect Prediction

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Mining the Technical Roles of GitHub Users

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Manifold embedded distribution adaptation for cross‐project defect prediction

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Isolation Forest Filter to Simplify Training Data for Cross-Project Defect Prediction

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Cross-Project Dynamic Defect Prediction Model for Crowdsourced test

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Heterogeneous Defect Prediction through Joint Metric Selection and Matching

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eCPDP: Early Cross-Project Defect Prediction

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Heterogeneous Defect Prediction through Correlation-Based Selection of Multiple Source Projects and Ensemble Learning

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Improving Cross-Project Defect Prediction Methods with Data Simplification

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Collective Personalized Change Classification With Multiobjective Search

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Cross-Project and Within-Project Semisupervised Software Defect Prediction: A Unified Approach

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An Approach for the Prediction of Number of Software Faults Based on the Dynamic Selection of Learning Techniques

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A Novel Class-Imbalance Learning Approach for Both Within-Project and Cross-Project Defect Prediction

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Effort-Aware Just-in-Time Bug Prediction for Mobile Apps Via Cross-Triplet Deep Feature Embedding

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Cross-Project Defect Prediction via Landmark Selection-Based Kernelized Discriminant Subspace Alignment

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Adjusted Trust Score: A Novel Approach for Estimating the Trustworthiness of Software Defect Prediction Models

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HYDRA: Massively Compositional Model for Cross-Project Defect Prediction

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The Use of Summation to Aggregate Software Metrics Hinders the Performance of Defect Prediction Models

inferred← citeshttps://doi.org/10.1109/tse.2017.2724538openalex-snapshot

A Comparative Study to Benchmark Cross-Project Defect Prediction Approaches

inferred← citeshttps://doi.org/10.1109/tse.2017.2731766openalex-snapshot

MAHAKIL: Diversity Based Oversampling Approach to Alleviate the Class Imbalance Issue in Software Defect Prediction

inferred← citeshttps://doi.org/10.1109/tse.2017.2780222openalex-snapshot

On the Multiple Sources and Privacy Preservation Issues for Heterogeneous Defect Prediction

inferred← citeshttps://doi.org/10.1109/tse.2019.2920771openalex-snapshot

Deep Transfer Bug Localization

inferred← citeshttps://doi.org/10.1109/tse.2020.2968520openalex-snapshot

An Empirical Study on Heterogeneous Defect Prediction Approaches

inferred← citeshttps://doi.org/10.1109/tse.2020.3001739openalex-snapshot

Revisiting Supervised and Unsupervised Methods for Effort-Aware Cross-Project Defect Prediction

inferred← citeshttps://doi.org/10.1109/tse.2026.3692541openalex-snapshot

Federated and Privacy-Preserving Cross-Project Defect Prediction With Heterogeneous Features

inferred← citeshttps://doi.org/10.1109/upcon.2017.8251131openalex-snapshot

A literature review on cross project defect prediction

inferred← citeshttps://doi.org/10.1109/wisa.2017.53openalex-snapshot

An Empirical Study on the Cross-Project Predictability of Continuous Integration Outcomes

inferred← citeshttps://doi.org/10.1142/s0218194016400155openalex-snapshot

An Empirical Study of Ranking-Oriented Cross-Project Software Defect Prediction

inferred← citeshttps://doi.org/10.1145/2593868.2593870openalex-snapshot

A new metric for predicting software change using gene expression programming

inferred← citeshttps://doi.org/10.1145/2597073.2597078openalex-snapshot

Towards building a universal defect prediction model

inferred← citeshttps://doi.org/10.1145/2786805.2786813openalex-snapshot

Heterogeneous cross-company defect prediction by unified metric representation and CCA-based transfer learning

inferred← citeshttps://doi.org/10.1145/2875913.2875944openalex-snapshot

Cross-Project Software Defect Prediction Using Feature-Based Transfer Learning

inferred← citeshttps://doi.org/10.1145/2931037.2931039openalex-snapshot

Mutation-aware fault prediction

inferred← citeshttps://doi.org/10.1145/2950290.2950306openalex-snapshot

When should internal interfaces be promoted to public?

inferred← citeshttps://doi.org/10.1145/3127005.3127015openalex-snapshot

On Applicability of Cross-project Defect Prediction Method for Multi-Versions Projects

inferred← citeshttps://doi.org/10.1145/3180155.3180197openalex-snapshot

Is "better data" better than "better data miners"?

inferred← citeshttps://doi.org/10.1145/3180155.3182520openalex-snapshot

MAHAKIL

inferred← citeshttps://doi.org/10.1145/3183339openalex-snapshot

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

inferred← citeshttps://doi.org/10.1145/3196321.3196331openalex-snapshot

Cross version defect prediction with representative data via sparse subset selection

inferred← citeshttps://doi.org/10.1145/3239235.3240501openalex-snapshot

Identifying unmaintained projects in github

inferred← citeshttps://doi.org/10.1145/3273934.3273938openalex-snapshot

Cross-Version Defect Prediction using Cross-Project Defect Prediction Approaches

inferred← citeshttps://doi.org/10.1145/3302541.3313101openalex-snapshot

Can we Predict Performance Events with Time Series Data from Monitoring Multiple Systems?

inferred← citeshttps://doi.org/10.1145/3368926.3369711openalex-snapshot

Unsupervised methods for Software Defect Prediction

inferred← citeshttps://doi.org/10.1145/3377811.3380360openalex-snapshot

Understanding the automated parameter optimization on transfer learning for cross-project defect prediction

inferred← citeshttps://doi.org/10.1145/3377811.3380389openalex-snapshot

Software visualization and deep transfer learning for effective software defect prediction

inferred← citeshttps://doi.org/10.1145/3590837.3590918openalex-snapshot

A Review on Software Defect Prediction Using Machine Learning

inferred← citeshttps://doi.org/10.1155/2021/5558561openalex-snapshot

Research on Cross-Company Defect Prediction Method to Improve Software Security

inferred← citeshttps://doi.org/10.1155/2022/2320447openalex-snapshot

Cross-Project Defect Prediction Based on Two-Phase Feature Importance Amplification

inferred← citeshttps://doi.org/10.11591/eei.v11i5.3698openalex-snapshot

The impact of training data selection on the software defect prediction performance and data complexity

inferred← citeshttps://doi.org/10.11591/eei.v13i3.5258openalex-snapshot

Cross-project software defect prediction through multiple learning

inferred← citeshttps://doi.org/10.11591/ijeecs.v27.i2.pp990-1006openalex-snapshot

A recommendation system of training data selection method for cross-project defect prediction

inferred← citeshttps://doi.org/10.11606/t.55.2018.tde-21032018-163840openalex-snapshot

Cross-project defect prediction with meta-Learning

inferred← citeshttps://doi.org/10.1186/2193-1801-2-627openalex-snapshot

Identifying influential metrics in the combined metrics approach of fault prediction

inferred← citeshttps://doi.org/10.13016/m2mc35openalex-snapshot

A Comparison of Transfer Learning Algorithms for Defect and Vulnerability Detection

inferred← citeshttps://doi.org/10.14569/ijacsa.2023.0141290openalex-snapshot

LPDA: Cross-Project Software Defect Prediction Approach via Locality Preserving and Distribution Alignment

inferred← citeshttps://doi.org/10.17559/tv-20220421110027openalex-snapshot

A Cross-project Defect Prediction Model Using Feature Transfer and Ensemble Learning

inferred← citeshttps://doi.org/10.18293/seke2015-132openalex-snapshot

An empirical study on predicting defect numbers

inferred← citeshttps://doi.org/10.18293/seke2016-039openalex-snapshot

Self-learning Change-prone Class Prediction

inferred← citeshttps://doi.org/10.18293/seke2016-047openalex-snapshot

A Ranking-Oriented Approach to Cross-Project Software Defect Prediction: An Empirical Study

inferred← citeshttps://doi.org/10.18293/seke2016-120openalex-snapshot

A Multi-Source TrAdaBoost Approach for Cross-Company Defect Prediction

inferred← citeshttps://doi.org/10.18293/seke2017-043openalex-snapshot

A Data Filtering Method Based on Agglomerative Clustering

inferred← citeshttps://doi.org/10.19153/cleiej.19.3.4openalex-snapshot

Feature Subset Selection and Instance Filtering for Cross-project Defect Prediction - Classification and Ranking

inferred← citeshttps://doi.org/10.2139/ssrn.5151247openalex-snapshot

Cross-Project Software Defect Prediction Based on Iosmote-Psbp

inferred← citeshttps://doi.org/10.22266/ijies2022.0430.08openalex-snapshot

Improved SMOTE and Optimized Siamese Neural Networks for Class Imbalanced Heterogeneous Cross Project Defect Prediction

inferred← citeshttps://doi.org/10.2991/icmmcce-17.2017.131openalex-snapshot

Software Defect Prediction Model Research for Network and Cloud Software Development

inferred← citeshttps://doi.org/10.32604/cmc.2023.046187openalex-snapshot

Cross-Project Software Defect Prediction Based on SMOTE and Deep Canonical Correlation Analysis

inferred← citeshttps://doi.org/10.3390/fi13080216openalex-snapshot

Cross-Project Defect Prediction Method Based on Manifold Feature Transformation

inferred← citeshttps://doi.org/10.3390/s21227535openalex-snapshot

An Empirical Study of Training Data Selection Methods for Ranking-Oriented Cross-Project Defect Prediction

inferred← citeshttps://doi.org/10.3390/sym11020212openalex-snapshot

Empirical Study of Software Defect Prediction: A Systematic Mapping

inferred← citeshttps://doi.org/10.3390/sym14020401openalex-snapshot

Cross-Project Defect Prediction Considering Multiple Data Distribution Simultaneously

inferred← citeshttps://doi.org/10.3390/sym15101934openalex-snapshot

Cross-Version Software Defect Prediction Considering Concept Drift and Chronological Splitting

inferred← citeshttps://doi.org/10.36227/techrxiv.19517029openalex-snapshot

Improving Transfer Learning for Cross Project Defect Prediction

inferred← citeshttps://doi.org/10.36227/techrxiv.19517029.v1openalex-snapshot

Improving Transfer Learning for Cross Project Defect Prediction

inferred← citeshttps://doi.org/10.36227/techrxiv.19517029.v2openalex-snapshot

Improving Transfer Learning for Cross Project Defect Prediction

inferred← citeshttps://doi.org/10.36676/jrps.v16.i4.330openalex-snapshot

Just-In-Time Static Analysis Using Predictive Models for Early Detection of Software Regression Patterns

inferred← citeshttps://doi.org/10.3934/mbe.2020054openalex-snapshot

Heterogeneous cross-project defect prediction with multiple source projects based on transfer learning

inferred← citeshttps://doi.org/10.4108/eai.13-7-2018.159974openalex-snapshot

Prediction of Cross Project Defects using Ensemble based Multinomial Classifier

inferred← citeshttps://doi.org/10.47836/pjst.30.3.23openalex-snapshot

Classification of Fault Prediction: A Mapping Study

inferred← citeshttps://doi.org/10.48550/arxiv.1405.0773openalex-snapshot

Simplification of Training Data for Cross-Project Defect Prediction

inferred← citeshttps://doi.org/10.48550/arxiv.1411.4228openalex-snapshot

Towards Cross-Project Defect Prediction with Imbalanced Feature Sets

inferred← citeshttps://doi.org/10.48550/arxiv.1612.09065openalex-snapshot

TDSelector: A Training Data Selection Method for Cross-Project Defect Prediction

inferred← citeshttps://doi.org/10.48550/arxiv.1705.03697openalex-snapshot

Is "Better Data" Better than "Better Data Miners"? (On the Benefits of Tuning SMOTE for Defect Prediction)

inferred← citeshttps://doi.org/10.48550/arxiv.1802.06025openalex-snapshot

A Systematic Study of Cross-Project Defect Prediction With Meta-Learning

inferred← citeshttps://doi.org/10.48550/arxiv.1901.08429openalex-snapshot

Transfer-Learning Oriented Class Imbalance Learning for Cross-Project Defect Prediction

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