Identifying Similar Software Datasets through Fuzzy Inference System

Author

Anwar, Sohel ; Rana, Z.A. ; Awais, Mian M.

Author_Institution

Sch. of Sci. & Eng. (SSE), Dept. of Comput. Sci., LUMS, Lahore, Pakistan

fYear

2012

fDate

17-19 Dec. 2012

Firstpage

181

Lastpage

187

Abstract

Similar software have similar software measurements. Defect data from one software can be used to anticipate defects in a similar software. Although, not many defect datasets are made public in software engineering domain, PROMISE repository is a reasonable collection of software data. This paper presents a two step approach to identify similar software and applies the proposed technique to find similar datasets in PROMISE repository. As step 1, the approach generates associations rules for each dataset to determine dataset´s behavior in terms of frequent patterns. As step 2, overlap between the association rules is calculated using Fuzzy Inference Systems (FIS). The FIS generated for the study have been expert-based as well as auto-generated. Similarity between 28 dataset pairs has been found KC2 and PC1 turned out to be most similar datasets with 86% similarity using Mamdani, 92% with Sugeno models. Results from expert-based and auto generated FIS have been comparable.

Keywords

data handling; data mining; fuzzy reasoning; software reliability; Mamdani model; PROMISE repository; Sugeno model; association rules; expert-based system; fuzzy inference system; software data collection; software dataset identification; software engineering domain; software measurement; Association rules; Fuzzy logic; Pragmatics; Software; Software measurement; Testing; FIS; association rules; dataset similarity; fuzzy system; software measures; software similarity;

fLanguage

English

Publisher

ieee

Conference_Titel

Frontiers of Information Technology (FIT), 2012 10th International Conference on

Conference_Location

Islamabad

Print_ISBN

978-1-4673-4946-8

Type

conf

DOI

10.1109/FIT.2012.40

Filename

6424319