Balancing the trade-off between accuracy and interpretability in software defect prediction

Mori, Toshiyuki; Uchihira, Naoshi

doi:10.1007/s10664-018-9638-1

Cited by 58 publications

(34 citation statements)

References 61 publications

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“…Gong et al [74] investigated the impact of class overlap and class imbalance problems on defect prediction, and then present an improved approach (IKMCCA) to solve those problems in order to improve defect prediction performance for within project defect prediction (WPDP) and cross project defect prediction (CPDP). Tantithamthavorn et al [175,265] conducted an empirical study to investigate how class rebalancing techniques impact the performance and interpretation of defect prediction. They also explored experimental settings that can help rebalancing techniques achieve the best performance in defect prediction.…”

Section: Sotware Maintenancementioning

confidence: 99%

Predictive Models in Software Engineering: Challenges and Opportunities

Yang

Xia

et al. 2022

ACM Trans. Softw. Eng. Methodol.

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Predictive models are one of the most important techniques that are widely applied in many areas of software engineering. There have been a large number of primary studies that apply predictive models and that present well-performed studies in various research domains, including software requirements, software design and development, testing and debugging and software maintenance. This paper is a first attempt to systematically organize knowledge in this area by surveying a body of 421 papers on predictive models published between 2009 and 2020. We describe the key models and approaches used, classify the different models, summarize the range of key application areas, and analyze research results. Based on our findings, we also propose a set of current challenges that still need to be addressed in future work and provide a proposed research road map for these opportunities.

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Section: Sotware Maintenancementioning

confidence: 99%

Predictive Models in Software Engineering: Challenges and Opportunities

Yang

Xia

et al. 2022

ACM Trans. Softw. Eng. Methodol.

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“…[52] combined dimension reduction techniques with SVM, which leads to achieving significant good results. Recent works by Pandey et al [53] and Mori and Uchihira [54] proposed an augmented‐based Naive Bayes model and also stated the trade‐off between accuracy and interpretability.…”

Section: Related Workmentioning

confidence: 99%

Software defect prediction using K‐PCA and various kernel‐based extreme learning machine: an empirical study

2020

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Predicting defects during software testing reduces an enormous amount of testing effort and help to deliver a high‐quality software system. Owing to the skewed distribution of public datasets, software defect prediction (SDP) suffers from the class imbalance problem, which leads to unsatisfactory results. Overfitting is also one of the biggest challenges for SDP. In this study, the authors performed an empirical study of these two problems and investigated their probable solution. They have conducted 4840 experiments over five different classifiers using eight NASA projects and 14 PROMISE repository datasets. They suggested and investigated the varying kernel function of an extreme learning machine (ELM) along with kernel principal component analysis (K‐PCA) and found better results compared with other classical SDP models. They used the synthetic minority oversampling technique as a sampling method to address class imbalance problems and k‐fold cross‐validation to avoid the overfitting problem. They found ELM‐based SDP has a high receiver operating characteristic curve over 11 out of 22 datasets. The proposed model has higher precision and F‐score values over ten and nine, respectively, compared with other state‐of‐the‐art models. The Mathews correlation coefficient (MCC) of 17 datasets of the proposed model surpasses other classical models' MCC.

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“…The new classifier obtained better result compared to the traditional classifier through a diffusion function that is built on the vibration of string. The new classifier is proposed as a solution to the short supply of crossproject training data and non-normal distributed attributes (Mori and Uchihira, 2019).…”

Section: Datasetmentioning

confidence: 99%

Machine Learning Techniques for Software Bug Prediction: A Systematic Review

Saharudin¹,

Koh²,

Kew³

2020

Journal of Computer Science

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The goal of software bug prediction is to identify the software modules that will have the likelihood to get bugs by using some fundamental project resources before the real testing starts. Due to high cost in correcting the detected bugs, it is advisable to start predicting bugs at the early stage of development instead of at the testing phase. There are many techniques and approaches that can be used to build the prediction models, such as machine learning. This technique is widely used nowadays because it can give accurate results and analysis. Therefore, we decided to perform a review of past literature on software bug prediction and machine learning so that we can understand better about the process of constructing the prediction model. Not only we want to see the machine learning techniques that past researchers used, we also assess the datasets, metrics and performance measures that are used during the development of the models. In this study, we have narrowed down to 31 main studies and six types of machine learning techniques have been identified. Two public datasets are found to be frequently used and object-oriented metrics are the highly chosen metrics for the prediction model. As for the performance measure, both graphical and numerical measures are often used to evaluate the performance of the models. From the results, we conclude that the machine learning technique can predict the bug, but there are not many applications in this area that exist nowadays. There are a few challenges in constructing the prediction model. Thus, more studies need to be carried out so that a well-formed result is obtained. We also provide a recommendation for future research based on the results we got from this study.

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Balancing the trade-off between accuracy and interpretability in software defect prediction

Cited by 58 publications

References 61 publications

Predictive Models in Software Engineering: Challenges and Opportunities

Predictive Models in Software Engineering: Challenges and Opportunities

Software defect prediction using K‐PCA and various kernel‐based extreme learning machine: an empirical study

Machine Learning Techniques for Software Bug Prediction: A Systematic Review

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