Analysing Software Metrics for Accurate Dynamic Defect Prediction Models

Chamoli, Shilpee; Tenne, Gil; Bhatia, Sanjay

doi:10.17485/ijst/2015/v8is4/63111

Cited by 7 publications

(2 citation statements)

References 7 publications

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“…Software quality improvement through defect prediction has been relying on a wide variety of software metrics treated as features. To comprehend the relationship between diverse software metric sets for defect prediction, Chamoli et al [34] analyze the performance of prediction models based on various software metrics and conclude that software metrics may indeed affect the models' defect prediction accuracy.…”

Section: Software Metricmentioning

confidence: 99%

An Empirical Study of Software Metrics Diversity for Cross-Project Defect Prediction

Zhong

Song

et al. 2021

Mathematical Problems in Engineering

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Cross-project defect prediction (CPDP) is a mainstream method estimating the most defect-prone components of software with limited historical data. Several studies investigate how software metrics are used and how modeling techniques influence prediction performance. However, the software’s metrics diversity impact on the predictor remains unclear. Thus, this paper aims to assess the impact of various metric sets on CPDP and investigate the feasibility of CPDP with hybrid metrics. Based on four software metrics types, we investigate the impact of various metric sets on CPDP in terms of F-measure and statistical methods. Then, we validate the dominant performance of CPDP with hybrid metrics. Finally, we further verify the CPDP-OSS feasibility built with three types of metrics (orient-object, semantic, and structural metrics) and challenge them against two current models. The experimental results suggest that the impact of different metric sets on the performance of CPDP is significantly distinct, with semantic and structural metrics performing better. Additionally, trials indicate that it is helpful for CPDP to increase the software’s metrics diversity appropriately, as the CPDP-OSS improvement is up to 53.8%. Finally, compared with two baseline methods, TCA+ and TDSelector, the optimized CPDP model is viable in practice, and the improvement rate is up to 50.6% and 25.7%, respectively.

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Section: Software Metricmentioning

confidence: 99%

An Empirical Study of Software Metrics Diversity for Cross-Project Defect Prediction

Zhong

Song

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…It usually focuses on estimating the defect proneness of software modules, and helps software practitioners allocate limited testing resources to the parts that are most likely to contain defects. At present, many prevalent machine learning methods could be used for defect prediction [4][5][6][7][8] . One of the most commonly used methods is classification.…”

Section: Introductionmentioning

confidence: 99%

An Information Flow-based Feature Selection Method for Cross-Project Defect Prediction

Wu¹

2018

IJPE

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Software defect prediction (SDP) plays a significant part in identifying the most defect-prone modules before software testing and allocating limited testing resources. One of the most commonly used scenarios in SDP is classification. To guarantee the prediction accuracy, the classification models should first be trained appropriately. The training data could be obtained from historical software repositories, which may affect the performance of classification to a large extent. In order to improve the data quality, we propose a novel software feature selection method, which innovatively utilizes the information flows to perform causality analysis in the features of training datasets. More specifically, we conduct causality analysis between each feature metric and the labeled metric bug; then, based on the obtained feature ranking list, we select the top-k features to control redundancy. Finally, we choose the most suitable feature subset based on the F-measure. To demonstrate the effectiveness and practicability of the feature selection method, we select the Nearest Neighbor approach to construct a homogeneous training dataset, and utilize three commonly used classification models to implement comparison experiments. The final experimental results have verified the availability and validity of the feature selection method.

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Analytics in Testing Communication Systems

Shama¹

2018

Analytic Methods in Systems and Software Testing

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Analysing Software Metrics for Accurate Dynamic Defect Prediction Models

Cited by 7 publications

References 7 publications

An Empirical Study of Software Metrics Diversity for Cross-Project Defect Prediction

An Empirical Study of Software Metrics Diversity for Cross-Project Defect Prediction

An Information Flow-based Feature Selection Method for Cross-Project Defect Prediction

Analytics in Testing Communication Systems

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