A Software Defect Prediction Approach Based on BiGAN Anomaly Detection

Zhang, Shenggang; Jiang, Shujuan; Jiang, Shujuan

doi:10.1155/2022/5024399

Cited by 9 publications

(7 citation statements)

References 37 publications

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“…Finally, the data of each cluster is combined to get the final data. In order to more clearly illustrate our approach, there is dataset D = {[ [2,3,6,8,9,12], [4][5][6][8][9][10], [4,5,8,9,12]]} that contains three instances. Each instance in dataset D is represented by six features.…”

Section: Hfdra Methodmentioning

confidence: 99%

“…Software defect prediction (SDP) can discover hidden defects in software modules in advance and then reasonably allocate test resources. It can improve test efficiency and reduce software development costs [1,2]. At present, most of the research works are to predict whether the modules in the software project contain defects, which can be regarded as a binary classification problem.…”

Section: Introductionmentioning

confidence: 99%

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A Software Defect Prediction Approach Based on Hybrid Feature Dimensionality Reduction

Zhang

Jiang

2023

Scientific Programming

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Software defect prediction (SDP) is designed to assist software testing, which can reasonably allocate test resources to reduce costs and improve development efficiency. In order to improve the prediction performance, researchers have designed many defect-related features for SDP. However, feature redundancy (FR) and irrelevance caused by the increasing dimensions of data will greatly degrade the performance of defect prediction. In order to solve the problems, researchers have proposed various data dimensionality reduction methods. These methods can be simply divided into two categories of methods: feature selection and feature extraction. However, the two categories of methods have their own advantages and limitation. In this paper, we propose a Hybrid Feature Dimensionality Reduction Approach (HFDRA) for SDP, which combines the two different kinds of methods, to improve the performance of SDP. HFDRA approach can be divided into two stages: feature selection and feature extraction. First, HFDRA divides the original features into several feature subsets through a clustering algorithm in the feature selection stage. Then, in the feature extraction stage, kernel principal component analysis (KPCA) is used to reduce the dimensionality of each feature subset. Finally, the reduced-dimensional data is used to build the prediction model. In the empirical study, we use 22 projects from AEEEM, SOFTLAB, MORP, and ReLink as experiment object. In this paper, we first compare our approach with seven baseline methods and three state-of-the-art methods. Then, we analyze the relationship between FR and prediction performance. Experiment results show that our approach outperforms the state-of-the-art data dimensionality reduction methods for defect prediction.

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Section: Hfdra Methodmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Software Defect Prediction Approach Based on Hybrid Feature Dimensionality Reduction

Zhang

Jiang

2023

Scientific Programming

Self Cite

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“…It is possible to compensate for the lack of data on numerous parameters by employing approximation data sets [12], [13]. Researchers have [33] built many models that make use of (easily measurable qualities) in order to properly forecast the traits that are harder to measure. For example, to determine the most efficient mathematical models for a certain kind of software system or a given task, such as maintenance.…”

Section: Related Workmentioning

confidence: 99%

A proposed model for detecting defects in software projects

Nabil Mahmoud,

Abdelaziz,

Santos

et al. 2024

IJEECS

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<span lang="EN-GB">Defective modules that cause software execution failures are common in large software projects. Source code for a significant number of modules may be found in several software repositories. This software repository includes each module’s software metrics and the module’s faulty status. Software companies face a considerable problem detecting defects in sizeable and complex programming code. In addition, many international reports, such as the comprehensive human appraisal for originating (CHAOS) report, have mentioned that there are countless reasons for the failure of software projects, including the inability to detect errors and defects in the programming code of those projects at an early stage. This research employs a statistical analysis technique to reveal the characteristics that indicate the faulty status of software modules. It is recommended that statistical analysis models derived from the retrieved information be merged with existing project metrics and bug data to improve prediction. When all algorithms are merged with weighted votes, the results indicate enhanced prediction abilities. The proposed statistical analysis outperforms the state-of-the-art method (association rule, decision tree, Naive Bayes, and neural network) in terms of accuracy by 9.1%, 10.3%, 13.1%, and 13.1%, respectively.</span>

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“…Many kinds of models have been used to tackle SDP. Researchers have proposed using supervised models [63], [64], [65], semi-supervised models [66], [67], unsupervised models [23], tasks specific models such as BugCache [22] and even approaching the problem as an anomaly detection problem [20], [21].…”

Section: B Metrics Prediction Granularity and Approaches To Sdpmentioning

confidence: 99%

Empirical Study: How Issue Classification Influences Software Defect Prediction

et al. 2023

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Software defect prediction aims to identify potentially defective software modules to better allocate limited quality assurance resources. Practitioners often do this by utilizing supervised models trained using historical data. This data is gathered by mining version control and issue tracking systems. Version control commits are linked to issues they address. If the linked issue is classified as a bug report, the change is considered as bug fixing. The problem arises from the fact that issues are often incorrectly classified within issue tracking systems. This introduces noise into the gathered datasets. In this paper, we investigate the influence issue classification has on software defect prediction dataset quality and resulting model performance. To do this, we mine data from 7 popular open-source repositories, create issue classification and software defect prediction datasets for each of them. We investigate issue classification using four different methods; a simple keyword heuristic, an improved keyword heuristic, the FastText model and the RoBERTa model. Our results show that using the RoBERTa model for issue classification produces the best software defect prediction datasets, containing on average 14.3641% of mislabeled instances. SDP models trained on such datasets achieve superior performance, to those trained on SDP datasets created using other issue classification methods, in 65 out of 84 experiments, with 55 of them being statistically relevant. Furthermore, in 17 out of 28 experiments we could not show a statistically relevant performance difference between SDP models trained on RoBERTa derived software defect prediction datasets and those created using manually labeled issues.

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A Software Defect Prediction Approach Based on BiGAN Anomaly Detection

Cited by 9 publications

References 37 publications

A Software Defect Prediction Approach Based on Hybrid Feature Dimensionality Reduction

A Software Defect Prediction Approach Based on Hybrid Feature Dimensionality Reduction

A proposed model for detecting defects in software projects

Empirical Study: How Issue Classification Influences Software Defect Prediction

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