Open AccessFuzzy Logic Based Model to Predict Per Phase Software Defect
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i1006.0789s19
Subject(s) - reliability engineering , software sizing , software metric , computer science , verification and validation , software construction , software reliability testing , software , software quality , fuzzy logic , measure (data warehouse) , software development , software measurement , data mining , artificial intelligence , statistics , mathematics , engineering , programming language
Software reliability is expressed as the probability of software to function properly under specified condition for a specified time period. A basic method to evaluate the software reliability is to check the presence of defects in the software. The presence of defect can be calculated as defect density measured defined as total number of defects present in the software divided by the size of the software. The paper proposes a fuzzy logic based model to predict per phase software defect density. The model uses 3 relevant software metrics per SDLC phase. Defect density prediction is a useful measure, which indicates the critical modules of the project and helps software teams to plan their resources in an efficient manner. The proposed model results are better in comparison with existing literature in the same domain when compared using MRE performance measure on 20 project dataset.