Open Access

ARTICLE

Mining Software Repository for Cleaning Bugs Using Data Mining Technique

Nasir Mahmood¹, Yaser Hafeez¹, Khalid Iqbal², Shariq Hussain³, Muhammad Aqib¹, Muhammad Jamal⁴, Oh-Young Song^5,*

1 University Institute of Information Technology, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, 46000, Pakistan
2 Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, 43600, Pakistan
3 Department of Software Engineering, Foundation University Islamabad, Islamabad, 44000, Pakistan
4 Department of Mathematics and Statistics, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, 46000, Pakistan
5 Department of Software, Sejong University, Seoul, 05006, Korea

* Corresponding Author: Oh-Young Song. Email:

Computers, Materials & Continua 2021, 69(1), 873-893. https://doi.org/10.32604/cmc.2021.016614

Received 06 January 2021; Accepted 06 April 2021; Issue published 04 June 2021

Abstract

Despite advances in technological complexity and efforts, software repository maintenance requires reusing the data to reduce the effort and complexity. However, increasing ambiguity, irrelevance, and bugs while extracting similar data during software development generate a large amount of data from those data that reside in repositories. Thus, there is a need for a repository mining technique for relevant and bug-free data prediction. This paper proposes a fault prediction approach using a data-mining technique to find good predictors for high-quality software. To predict errors in mining data, the Apriori algorithm was used to discover association rules by fixing confidence at more than 40% and support at least 30%. The pruning strategy was adopted based on evaluation measures. Next, the rules were extracted from three projects of different domains; the extracted rules were then combined to obtain the most popular rules based on the evaluation measure values. To evaluate the proposed approach, we conducted an experimental study to compare the proposed rules with existing ones using four different industrial projects. The evaluation showed that the results of our proposal are promising. Practitioners and developers can utilize these rules for defect prediction during early software development.

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Citations