Open AccessSoftware Fault Prediction Using Two -Stage Data Preprocessing Method
Author(s) -
Varsha G. Palatse,
V. S. Nandedkar
Publication year - 2016
Publication title -
ijars international journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2455-1481
DOI - 10.20908/ijarsije.v2i1.10945
Subject(s) - systems development life cycle , reliability engineering , computer science , software reliability testing , avionics software , software quality , software development , fault detection and isolation , software construction , software development process , software fault tolerance , software sizing , regression testing , fault coverage , software , verification and validation , engineering , operating system , artificial intelligence , electronic circuit , electrical engineering , actuator , operations management
Software testing plays important role in software development life cycle (SDLC) while dealing with critical business applications. From SDLC 70% of time and cost is used for testing only, it is time consuming and costly phase. For this one of the cost effective technique can be used which predicts a module as fault-prone and non-fault prone prior to testing. Predicting a safe module during testing and after development increases the cost of projects. As need to allocate better-test resources for module. This process may take most of the time in preparation and may lose some modules untested which cause accidental failure. Defective software modules cause software failures, increase development and maintenance costs, and decrease customer satisfaction. In this research, we present a novel fault prediction technique that reduces the probability of false (pf) and increases the precision for detection of faulty modules. Software Reliability is becoming an essential attribute of any software system. To improve software quality and testing efficiency by constructing predictive models from code attributes to enable a timely identification of fault-prone modules.Software defect prediction model helps in early detection of faults and contribute to their efficient removal and producing a reliable software system.