Abstract
This research paper explores cross-project defect prediction as a means to improve software quality assurance (SQA) practices. Traditionally within-project defect prediction methods face challenges due to limited training data and project-specific characteristics. In contrast, cross-project defect prediction leverages knowledge transfer from multiple projects to develop more robust and generalizable defect prediction models. The study investigates various knowledge transfer strategies, such as instance-based, feature-based, and model-based transfer, and conducts extensive experiments on diverse software repositories. The results demonstrate that knowledge transfer techniques outperform traditional methods, offering higher accuracy and improved generalization to unseen projects. The paper also analyzes key factors influencing cross-project defect prediction success, providing practical guidelines for real-world SQA applications. By enabling effective defect prediction, this research contributes to enhancing software quality and maintenance.
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References
Balaram A, Vasundra S (2022) Software fault detection using multi-distinguished-features sampling with ensemble random forest classifier. Int J Intell Eng Syst 15(5): 494–505
Pahal A, Chillar R (2017) A hybrid approach for SFP using ANN and simplified swarm optimization. Int J Adv Res Comput Commun Eng 6(3)
Abubakar A, Jarallah AGA, Moataz A (2006) Can cohesion predict fault density?. IEEE 1:890–894
Anushree A, Ruchika M (2022) Cross project defect prediction for open source softwares. Int J Inform Technol 14:587–601
Grishma B, Anjali C (2015) Software root cause prediction using clustering techniques. In: Global conference on communication technologies
Prabha C, Shivakumar N (2020) Software defect prediction using machine learning techniques. In: International conference on trends in electronics and informatics
Chidamber S, Kemerer C (1994) A metric suite for object oriented design. IEEE Trans Softw Eng 20(9)
Cao H (2020) A systematic study for learning based software defect prediction. IOP Conf Ser
Tong H, Liu B, Wang S (2019) Kernel spectral embedding transfer ensemble for heterogeneous defect prediction.[s.l.] IEEE Trans Softw Eng 47(9)
Ayushi K et al (2022) Software fault prediction using machine learning models. In: OITS International conference on information technology
Lessmann S et al (2008) Benchmarking classification models for software defect prediction: a proposed framework and novel findings. IEEE Trans Softw Eng
Anwar N, Kar S (2019) Review paper on various software testing techniques and strategies. Global J Comput Sci Technol: Comput Softw Data Eng 19(2)
Kalaivani N, Beena R (2018) Overview of software defect prediction using machine learning algorithms. Int J Pure and Appl Mathem 18(20)
Qasem OA, Akour M, Alenezi M (2020) The influence of deep learning algorithms factors in software fault prediction. IEEE Access 8
Sanchita P, Kuldeep K (2023) Software fault prediction for imbalanced data: a survey on recent developments. Proc Comput Sci 218
Patchaiammal P, Thirumalaiselvi R (2019) Software fault prediction exploration using machine learning techniques. Int J Recent Technol Eng 7(6S3)
Samantaray R, Das H (2023) Performance analysis of machine learning algorithms using bagging ensemble technique for software fault prediction. 6th International conference on information systems and computer networks
Khan RU et al (2020) Software defect prediction via deep learning. Int J Innov Technol Explor Eng
Rana ZA, Mian MA, Shamail S (2009) An FIS for early detection of defect prone modules. In: Intelligent computing
Zhao R et al (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213–237
Kumar S, Ranjan P (2017) A comprehensive analysis for software fault detection an prediction using computational intelligence techniques. Int J Comput Intell Sys 13(1):65–78
Mahapatra S, Mishra S (2020) Usage of machine learning in software testing. In: Automated software engineering: a deep learning based approach. Learning and analytics in intelligent system
Mishra S (2020) Usage of machine learning in software testing. Automat Softw Eng: A Deep Learn Based Approach 39–54
Saharudin S, Wei K, Na K (2020) Machine learning techniques for software systematic review. J Comput Sci
Prachi S (2016) Analysis of test management, functional and load testing tools. Int J Scient Res Comput Sci Eng Inform Technol 1(1)
Prachi S (2022) Cross Project defect prediction using deep learning techniques. In: International conference on artificial intelligence and big data analytics
Jing X-Y et al (2014) Dictionary learning based software defect prediction. In: Proceedings of the 36th international conference on software engineering
Jun Z (2010) Cost-sensitive boosting neural networks for software defect prediction. Expert Syst with Appl 37(6):4537
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Sasankar, P., Sakarkar, G. (2024). Cross-Project Defect Prediction: Leveraging Knowledge Transfer for Improved Software Quality Assurance. In: Shaw, R.N., Siano, P., Makhilef, S., Ghosh, A., Shimi, S.L. (eds) Innovations in Electrical and Electronic Engineering. ICEEE 2023. Lecture Notes in Electrical Engineering, vol 1115. Springer, Singapore. https://doi.org/10.1007/978-981-99-8661-3_22
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DOI: https://doi.org/10.1007/978-981-99-8661-3_22
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