Prediction of defect severity by mining software project reports

Rajni Jindal (), Ruchika Malhotra () and Abha Jain ()
Additional contact information
Rajni Jindal: Delhi Technological University
Ruchika Malhotra: Delhi Technological University
Abha Jain: Delhi Technological University

International Journal of System Assurance Engineering and Management, 2017, vol. 8, issue 2, No 12, 334-351

Abstract: Abstract With ever increasing demands from the software organizations, the rate of the defects being introduced in the software cannot be ignored. This has now become a serious cause of concern and must be dealt with seriously. Defects which creep into the software come with varying severity levels ranging from mild to catastrophic. The severity associated with each defect is the most critical aspect of the defect. In this paper, we intend to predict the models which will be used to assign an appropriate severity level (high, medium, low and very low) to the defects present in the defect reports. We have considered the defect reports from the public domain PITS dataset (PITS A, PITS C, PITS D and PITS E) which are being popularly used by NASA’s engineers. Extraction of the relevant data from the defect reports is accomplished by using text mining techniques and thereafter model prediction is carried out by using one statistical method i.e. Multi-nominal Multivariate Logistic Regression (MMLR) and two machine learning methods viz. Multi-layer Perceptron (MLP) and Decision Tree (DT). The performance of the models has been evaluated using receiver operating characteristics analysis and it was observed that the performance of DT model is the best as compared to the performance of MMLR and MLP models.

Keywords: Defect prediction; Empirical validation; Machine learning; Text mining; InfoGain; Receiver operating characteristics; Statistical methods (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-016-0438-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:8:y:2017:i:2:d:10.1007_s13198-016-0438-y

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-016-0438-y

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().