node2defect: using network embedding to improve software defect prediction

Qu, Yu; Liu, Ting; Chi, Jianlei; Jin, Yingze; Cui, Di; He, Ancheng; Zheng, Qinghua

doi:10.1145/3238147.3240469

Cited by 24 publications

(35 citation statements)

References 18 publications

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“…Qu et al [16] also used a newly proposed network embedding technique and used automatically encoded class dependency relationships on low-dimensional vector space to improve software defect prediction, named node2defect. Tua and Danar Sunindyo [4] also added the process of selecting features using Rule Mining Association Methods (ARM) in the software defects prediction process and showed that using the Naive Bayesian (NB) method with ARM can improve the performance of the method using software metrics.…”

Section: Related Workmentioning

confidence: 99%

Software Defect Prediction Based on Elman Neural Network and Cuckoo Search Algorithm

Song

et al. 2021

Mathematical Problems in Engineering

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In software engineering, defect prediction is significantly important and challenging. The main task is to predict the defect proneness of the modules. It helps developers find bugs effectively and prioritize their testing efforts. At present, a lot of valuable researches have been done on this topic. However, few studies take into account the impact of time factors on the prediction results. Therefore, in this paper, we propose an improved Elman neural network model to enhance the adaptability of the defect prediction model to the time-varying characteristics. Specifically, we optimized the initial weights and thresholds of the Elman neural network by incorporating adaptive step size in the Cuckoo Search (CS) algorithm. We evaluated the proposed model on 7 projects collected from public PROMISE repositories. The results suggest that the contribution of the improved CS algorithm to Elman neural network model is prominent, and the prediction performance of our method is better than that of 5 baselines in terms of F-measure and Cliff’s Delta values. The F-measure values are generally increased with a maximum growth rate of 49.5% for the POI project.

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Section: Related Workmentioning

confidence: 99%

Software Defect Prediction Based on Elman Neural Network and Cuckoo Search Algorithm

Song

et al. 2021

Mathematical Problems in Engineering

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“…With the extensive application of deep learning technology in various fields, its powerful feature generation ability has also been used for defect prediction [11][12][13][14]. For example, Wang et al [13] generate the source code ASTs and automatically learn the program's hidden semantic and syntax features through a deep belief network.…”

Section: Cross-project Defect Predictionmentioning

confidence: 99%

“…Phan et al [33] propose transforming the source code into program control flow graphs (CFG) to extract deeper semantic features from the code. Qu et al [11] leverage a network embedding technique to automatically learn to encode the program's class dependency network structure into low-dimensional vector spaces to improve software defect prediction.…”

Section: Cross-project Defect Predictionmentioning

confidence: 99%

“…For example, the commonly used AEEEM data set involves two sets of software metrics. According to the existing practice [11][12][13], deep learning technology can provide structural and semantic metrics. In other words, there are at least four sets of metrics.…”

Section: Research Questionmentioning

confidence: 99%

“…Such techniques involve deepwalk [8], node2vec [9], and struc2vec [10]. Qu et al [11] automatically learn the node representation from the CDN using network embedding for software defect prediction and achieve quite appealing results.…”

Section: Introductionmentioning

confidence: 99%

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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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Executable Knowledge Graphs for Machine Learning: A Bosch Case of Welding Monitoring

Zheng

Zhou

et al. 2022

The Semantic Web – ISWC 2022

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node2defect: using network embedding to improve software defect prediction

Cited by 24 publications

References 18 publications

Software Defect Prediction Based on Elman Neural Network and Cuckoo Search Algorithm

Software Defect Prediction Based on Elman Neural Network and Cuckoo Search Algorithm

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

Executable Knowledge Graphs for Machine Learning: A Bosch Case of Welding Monitoring

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