The main aim of software development is to develop high quality software and high quality software is developed us in vast amount of software engineering data. The software engineering data can be used to gain empirically based understanding of software development. Software is ubiquitous in our daily life. It brings us great convenience and a big headache about software reliability as well: Software is never bug-free, and software bugs keep incurring monetary loss of even catastrophes. Data mining techniques are applied in building software fault prediction models for improving the software quality. Early identification of high-risk modules can assist in quality enhancement efforts to modules that are likely to have a high number of faults. This paper presents the data mining algorithms and techniques most commonly used to produce patterns and extract interesting information from software engineering data. The techniques are organized in seven sections: classification trees, association discovery, clustering, artificial neural networks, optimized set reduction, Bayesian belief networks, and visual data mining can be used to achieve high software reliability. The Reliability of any software utility software program is becoming so crucial in our everyday existence need; mistakes of the software application sadly continue to be common place to purpose machine disasters. In software program application software development the most time ingesting and hard challenge is to discover insects and join them. Then for solving this difficult trouble it is probably substantially advocated if we take a look at the bud and apprehend their conduct and their tendencies then stumble ion them robotically. Inside the software that consists of a massive code of statistics and in files. It is difficult for the builders to analyse the facts and find them. We're coming close to a facts mining approach to extract a useful understanding inside the large software program application and contribute this records for laptop virus detecting.
Volume 12 | Issue 4
Pages: 167-173
DOI: 10.5373/JARDCS/V12I4/20201429