TSTSS: A two-stage training subset selection framework for cross version defect prediction

Zhou, Xu; Li, Shuai; Luo, Xiapu; Liu, Jin; Zhang, Tao; Tang, Yutian; Xu, Jun; Yuan, Peipei; Keung, Jacky

doi:10.1016/j.jss.2019.03.027

Cited by 33 publications

(33 citation statements)

References 33 publications

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“…0.05/8 = 0.00625) to compute the significant difference of the model performance. This evaluation has been widely used for performance comparison in many defect prediction studies [38, 45, 58–60]. Friedman test is used to determine if there are statistically significant differences between multiple models, and Nemenyi test is performed to check which model differs significantly.…”

Section: Experiments Resultsmentioning

confidence: 99%

Manifold embedded distribution adaptation for cross‐project defect prediction

Sun

Jing

et al. 2020

IET softw.

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Cross‐project defect prediction (CPDP) technology refers to the constructing prediction model to predict the instance label of the target project by utilising labelled data from an external project. The challenge of CPDP methods is the distribution difference between the data from different projects. Transfer learning can transfer the knowledge from the source domain to the target domain with the aim to minimise the domain difference between different domains. However, most existing methods reduce the distribution discrepancy in the original feature space, where the features are high‐dimensional and non‐linear, which makes it hard to reduce the distribution distance between different projects. Moreover, previous works mainly consider marginal distribution or conditional distribution difference. In this study, the authors proposed a manifold embedded distribution adaptation (MDA) approach to narrow the distribution gap in manifold feature subspace. MDA maps source and target project data to manifold subspace and then joint distribution adaptation of conditional and marginal distributions is performed on manifold subspace. To evaluate the effectiveness of MDA, the authors perform extensive experiments on 20 public projects with three indicators. The experiment results show that MDA improves the average performance, but the improvement is not statistically significant in comparison to HYDRA (one of the baselines).

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Section: Experiments Resultsmentioning

confidence: 99%

Manifold embedded distribution adaptation for cross‐project defect prediction

Sun

Jing

et al. 2020

IET softw.

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“…Cross-version context is where the training data to predict the defects in the current version of a project is comprised of data from its prior versions [98,99]. For instance, in the Cross-version context, one uses versions V 1 ,V 2 ,.…”

Section: Sdp Contextsmentioning

confidence: 99%

Revisiting the Impact of Dependency Network Metrics on Software Defect Prediction

Gong,

Rajbahadur,

Hassan

et al. 2022

Preprint

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Software dependency network metrics extracted from the dependency graph of the software modules by the application of Social Network Analysis (SNA metrics) have been shown to improve the performance of the Software Defect prediction (SDP) models. However, the relative effectiveness of these SNA metrics over code metrics in improving the performance of the SDP models has been widely debated with no clear consensus. Furthermore, some of the common SDP scenarios like predicting the number of defects in a module (Defect-count) in Cross-version and Cross-project SDP contexts remain unexplored. Such lack of clear directive on the effectiveness of SNA metrics when compared to the widely used code metrics prevents us from potentially building better performing SDP models. Therefore, through a case study of 9 open source software projects across 30 versions, we study the relative effectiveness of SNA metrics when compared to code metrics across 3 commonly used SDP contexts (Within-project, Cross-version and Crossproject) and scenarios (Defect-count, Defect-classification (classifying if a module is defective) and Effort-aware (ranking the defective modules w.r.t to the involved effort)). We find the SNA metrics by themselves or along with code metrics improve the performance of SDP models over just using code metrics on 5 out of the 9 studied SDP scenarios (three SDP scenarios across three SDP contexts). However, we note that in some cases the improvements afforded by considering SNA metrics over or alongside code metrics might only be marginal, whereas in other cases the improvements could be potentially large. Based on these findings we suggest that the future work should: consider SNA metrics alongside code metrics in their SDP models; as well as consider Ego metrics and Global metrics, the two different types of the SNA metrics separately when training SDP models as they behave differently.

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“…Since the proposed NSCR model (5) does not have an analytical solution, we employ the variable splitting method [46,47] to solve it. We firstly reformulate the NSCR model ( 5) into a linear equality-constrained problem by introducing an auxiliary variable z:…”

Section: Optimizationmentioning

confidence: 99%

“…Then, the alternating direction method of multipliers(ADMM) [30] framework can be employed to solve the NSCR model (5).…”

Section: Optimizationmentioning

confidence: 99%

“…Pattern classification is a supervised learning problem in which the goal is to assign the test images to correct classes according to the labeled training images. It is a challenging computer vision and image understanding problem with numerous applications [1,2,3,4,5]. Denote the training images as a data matrix X = [X 1 , ..., X K ] ∈ R D×N from K different classes.…”

Section: Introductionmentioning

confidence: 99%

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Non-negative Sparse and Collaborative Representation for Pattern Classification

Xu¹,

Zhou²,

An³

et al. 2019

Preprint

Self Cite

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Sparse representation (SR) and collaborative representation (CR) have been successfully applied in many pattern classification tasks such as face recognition. In this paper, we propose a novel Non-negative Sparse and Collaborative Representation (NSCR) for pattern classification. The NSCR representation of each test sample is obtained by seeking a non-negative sparse and collaborative representation vector that represents the test sample as a linear combination of training samples. We observe that the non-negativity can make the SR and CR more discriminative and effective for pattern classification. Based on the proposed NSCR, we propose a NSCR based classifier for pattern classification. Extensive experiments on benchmark datasets demonstrate that the proposed NSCR based classifier outperforms the previous SR or CR based approach, as well as state-of-the-art deep approaches, on diverse challenging pattern classification tasks.

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TSTSS: A two-stage training subset selection framework for cross version defect prediction

Cited by 33 publications

References 33 publications

Manifold embedded distribution adaptation for cross‐project defect prediction

Manifold embedded distribution adaptation for cross‐project defect prediction

Revisiting the Impact of Dependency Network Metrics on Software Defect Prediction

Non-negative Sparse and Collaborative Representation for Pattern Classification

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