Defect Prediction Framework Using Neural Networks for Software Enhancement Projects
Vipul Vashisht, Manohar Lal, G. S. Sureshchandar
Journal of Advances in Mathematics and Computer Science · pp. 1–12 · Published 9 May 2016
10.9734/BJMCS/2016/26337Abstract
So far, various approaches have been proposed for effective and accurate prediction of software defects, yet most of these approaches have limited adoption in practice. The objective of this paper is to provide a framework which is expected to be more user-friendly, effective and acceptable for predicting the defects in multiple phases across software enhancement projects. This communication describes a process of applying computational intelligence technologies, in particular neural networks in formulating defect prediction models early in the software development life cycle. A series of empirical experiments are carried out based on input and output measures extracted from 50 'real world' project subsystems. In order to increase the adoption and make the prediction framework easily accessible to project managers, a graphical user interface (GUI) based tool has been designed and implemented that allows input data to be fed easily. The proposed framework uses historical data for training model and as a result provides a defect range (minimum, maximum) based output instead of a definite defect count based output. This is done in view of the fact that exact-count prediction has less probability of being correct as compared to range based predictions. The defect predictions can be used for taking informed decisions including prioritizing software testing efforts, planning additional round of code reviews, allocating human and computer resources, planning for risk mitigation strategy and other corrective actions. The claim of effectiveness of proposed framework is established through results of a comparative study, involving the proposed framework and some well-known models for software defect prediction.
Cited by 16
Sushant Kumar Pandey, Ravi Bhushan Mishra, Anil Kumar Tripathi · Expert Systems with Applications · 2020
Md Anwar Hossen, Md. Shariful Islam, Nurhafizah Abu Talip Yusof · Lecture Notes in Electrical Engineering · 2020
Haonan Tong, Bin Liu, Shihai Wang · Information and Software Technology · 2018
E. Sreedevi, S. Sivakumar, Soumya Ranjan Nayak · Lecture Notes in Networks and Systems · 2024
Manisha Vashisht, Brijesh Kumar · 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA) · 2020
Vipul Vashisht, Suraj Kamya, Manisha Vashisht · 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA) · 2020
Lam Thu Bui, Van Truong Vu, Bich Van Pham · 2022 14th International Conference on Knowledge and Systems Engineering (KSE) · 2022
Begüm ERKAL, Tülin ERÇELEBİ AYYILDIZ · Fırat Üniversitesi Mühendislik Bilimleri Dergisi · 2022
Emilia Sipos, Laura-Nicoleta Ivanciu · 2017 40th International Spring Seminar on Electronics Technology (ISSE) · 2017
Manisha Vashisht, Brijesh Kumar · Lecture Notes in Networks and Systems · 2021
Related research
Article metrics
Real usage data collected on this platform.
0
Page views
0
PDF downloads
0
Outbound clicks
16
Citations
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
No views recorded yet.
Traffic sources
Referring site, by host.
No traffic recorded yet.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.