NDLI: A comparative analysis between two techniques for the prediction of software defects: fuzzy and statistical linear regression

Please wait, while we are loading the content...

A comparative analysis between two techniques for the prediction of software defects: fuzzy and statistical linear regression

Content Provider	Springer Nature Link
Author	Valles Barajas, Fernando
Copyright Year	2015
Abstract	Software engineers should estimate the necessary resources (time, people, software tools among others) to satisfy software project requirements; this activity is carried out in the planning phase. The estimated time for developing software projects is a necessary element to establish the cost of software projects and to assign human resources to every phase of software projects. Most companies fail to finish software projects on time because of a poor estimation technique or the lack of the same. The estimated time must consider the time spent eliminating software defects injected during each of the software phases. A comparative analysis between two techniques (fuzzy linear regression and statistical linear regression) to perform software defect estimation is presented. These two techniques model uncertainty in a different way; statistical linear regression models uncertainty as randomness, whereas fuzzy linear regression models uncertainty as fuzziness. The main objective of this paper was to establish the kind of uncertainty associated with software defect prediction and to contrast these two prediction techniques. The KC1 NASA data set was used to do this analysis. Only six of the metrics included in KC1 data set and lines of code metric were used in this comparative analysis. Descriptive statistics was first used to have an overview of the main characteristics of the data set used in this research. Linearity property between predictor variables and the variable of interest number of defects was checked using scatter plots and Pearson’s correlation coefficient. Then the problem of multicollinearity was verified using inter-correlations among metrics and the variance inflation factor. Best subset regression was applied to detect the most influencing subset of predictor variables; this subset was later used to build fuzzy and statistical regression models. Linearity property between metrics and number of defects was confirmed. The problem of multicollinearity was not detected in the predictor variables. Best subset regression found that the subset composed of 5 variables was the most influencing subset. The analysis showed that the statistical regression model in general outperformed the fuzzy regression model. Techniques for making software defect prediction should be carefully employed in order to have quality plans. Software engineers should consider and understand a set of prediction techniques and know their weaknesses and strengths. At least, in the KC1 data set, the uncertainty in the software defect prediction model is due to randomness so it is reasonable to use statistical linear regression instead of fuzzy linear regression to build a prediction model.
Starting Page	277
Ending Page	287
Page Count	11
File Format	PDF
ISSN	16145046
Journal	Innovations in Systems and Software Engineering
Volume Number	11
Issue Number	4
e-ISSN	16145054
Language	English
Publisher	Springer London
Publisher Date	2015-07-30
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Fuzzy linear regression Statistical linear regression Software defect prediction Software Engineering Computing Methodologies Computer Applications
Content Type	Text
Resource Type	Article
Subject	Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

A dynamic capture–recapture model for software defect prediction

Monte Carlo methods in fuzzy linear regression

Learning better IV&V practices

Fuzzy statistics: regression and prediction

Monte Carlo methods in fuzzy linear regression II

Fuzzy linear regression model based on fuzzy scalar product

Dealing with variability within a family of domain-specific languages: comparative analysis of different techniques

Tracing requirements to defect reports: an application of information retrieval techniques

A heuristic-based regression test case prioritization approach for object-oriented programs

A comparative analysis between two techniques for the prediction of software defects: fuzzy and statistical linear regression

Similar Documents

A dynamic capture–recapture model for software defect prediction

Monte Carlo methods in fuzzy linear regression

Learning better IV&V practices

Fuzzy statistics: regression and prediction

Monte Carlo methods in fuzzy linear regression II

Fuzzy linear regression model based on fuzzy scalar product

Dealing with variability within a family of domain-specific languages: comparative analysis of different techniques

Tracing requirements to defect reports: an application of information retrieval techniques

A heuristic-based regression test case prioritization approach for object-oriented programs

A comparative analysis between two techniques for the prediction of software defects: fuzzy and statistical linear regression