Using Bandit Algorithms for Project Selection in Cross-Project Defect Prediction

Asano, Tsunaki; Tsunoda, Masateru; Toda, Kengo; Tahir, Amjed; Bennin, Kwabena Ebo; Nakasai, Keitaro; Monden, Akito; Matsumoto, Kenichi

doi:10.1109/icsme52107.2021.00074

Cited by 6 publications

(7 citation statements)

References 10 publications

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“…and encoded into added code embedding by Z 0 a ¼ A a←r � V a , where Z 0 a ∈ R d�n is the enhanced embedding of added code and � stands for the element-wise multiplication. Similarly, the enhanced embedding of removed code can be obtained by formula (5).…”

Section: Line Label Fusionmentioning

confidence: 99%

Just‐in‐time defect prediction enhanced by the joint method of line label fusion and file filtering

Zhang

Kuang

et al. 2023

IET Software

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Just-In-Time (JIT) defect prediction aims to predict the defect proneness of software changes when they are initially submitted. It has become a hot topic in software defect prediction due to its timely manner and traceability. Researchers have proposed many JIT defect prediction approaches. However, these approaches cannot effectively utilise line labels representing added or removed lines and ignore the noise caused by defectirrelevant files. Therefore, a JIT defect prediction model enhanced by the joint method of line label Fusion and file Filtering (JIT-FF) is proposed. Firstly, to distinguish added and removed lines while preserving the original software changes information, the authors represent the code changes as original, added, and removed codes according to line labels. Secondly, to obtain semantics-enhanced code representation, a cross-attentionbased line label fusion method to perform complementary feature enhancement is proposed. Thirdly, to generate code changes containing fewer defect-irrelevant files, the authors formalise the file filtering as a sequential decision problem and propose a reinforcement learning-based file filtering method. Finally, based on generated code changes, CodeBERT-based commit representation and multi-layer perceptron-based defect prediction are performed to identify the defective software changes. The experiments demonstrate that JIT-FF can predict defective software changes more effectively.

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Section: Line Label Fusionmentioning

confidence: 99%

Just‐in‐time defect prediction enhanced by the joint method of line label fusion and file filtering

Zhang

Kuang

et al. 2023

IET Software

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“…Takuya Asano et al (2021) [73] attempted to identify suitable training projects to improve the performance of the model. They used the Bandit Algorithm to identify the most suitable project.…”

Section: ) Projects and Metrics Selectionmentioning

confidence: 99%

“…[39] Supervised Learning It concluded that Large Software system can't be a good predictor for small software system. Bandit Algorithm (BA) to identify a suitable external project (2021) [73] Supervised Learning…”

Section: ) Projects and Metrics Selectionmentioning

confidence: 99%

Cross-Project Defect Prediction: A Literature Review

Pal

Sillitti²

2022

IEEE Access

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Background: Software defect prediction models aim at identifying the potential faulty modules of a software project based on historical data collected from previous versions of the same project. Due to the lack of availability of software engineering data from the same project, the researchers proposed crossproject defect prediction (CPDP) models where the data collected from one or more projects are used to predict faults in other project. There are a number of approaches proposed with different levels of success and very limited repeatability. Goals: The purpose of this paper is to investigate the existing studies of cross-project models for defect prediction. It synthesizes the literature focusing on characteristics such as project type, software metrics, data preprocessing techniques, features selection approaches, classifiers, and performance measures used. Method: This paper follows the well-known Systematic Literature Review (SLR) approach proposed by Barbara Kitchenham in 2007. Results: Our finding shows that most of the article was published between 2015 and 2021. Moreover, the studies are mostly based on open-source datasets and the software metrics used to create the models are mainly product metrics. We also found out that most studies attempted to improve their models improving data preprocessing and feature selection approaches. Furthermore, logistic regression followed by naive bayes and random forest are the most adopted classifier techniques in such models. Finally, the f-measure followed by recall and AUC are the most preferred evaluation measure used to evaluate the performance of the models. Conclusions: This study provides an overview of the different approaches used to improve the CPDP models analyzing the different techniques used for data preprocessing, feature selection, and the selection of the classifiers. Moreover, we identified some aspects that need further investigation.

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“…The study of Zhou et al [36] investigated the performances of a number of CPDP techniques and models with simple size models and observed that simple size models in most cases outperformed the complex and recently proposed CPDP techniques. Asano et al [37] applied bandit algorithms to help in selecting the most suitable projects for CPDP models. Our study aims to complement prior studies by aiming to improve the performance of existing CPDP models that adopts the NN filter approach.…”

Section: Cross-project Defect Predictionmentioning

confidence: 99%

An empirical study on the effectiveness of data resampling approaches for cross‐project software defect prediction

et al. 2021

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Cross-project defect prediction (CPDP), where data from different software projects are used to predict defects, has been proposed as a way to provide data for software projects that lack historical data. Evaluations of CPDP models using the Nearest Neighbour (NN) Filter approach have shown promising results in recent studies. A key challenge with defect-prediction datasets is class imbalance, that is, highly skewed datasets where nonbuggy modules dominate the buggy modules. In the past, data resampling approaches have been applied to within-projects defect prediction models to help alleviate the negative effects of class imbalance in the datasets. To address the class imbalance issue in CPDP, the authors assess the impact of data resampling approaches on CPDP models after the NN Filter is applied. The impact on prediction performance of five oversampling approaches (MAHAKIL, SMOTE, Borderline-SMOTE, Random Oversampling and ADASYN) and three undersampling approaches (Random Undersampling, Tomek Links and One-sided selection) is investigated and results are compared to approaches without data resampling. The authors examined six defect prediction models on 34 datasets extracted from the PROMISE repository. The authors' results show that there is a significant positive effect of data resampling on CPDP performance, suggesting that software quality teams and researchers should consider applying data resampling approaches for improved recall ( pd ) and g-measure prediction performance. However, if the goal is to improve precision and reduce false alarm ( pf) then data resampling approaches should be avoided.

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Using Bandit Algorithms for Project Selection in Cross-Project Defect Prediction

Cited by 6 publications

References 10 publications

Just‐in‐time defect prediction enhanced by the joint method of line label fusion and file filtering

Just‐in‐time defect prediction enhanced by the joint method of line label fusion and file filtering

Cross-Project Defect Prediction: A Literature Review

An empirical study on the effectiveness of data resampling approaches for cross‐project software defect prediction

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