Analysis and Design of Data Mining Techniques to Develop Software Reliability

Kumar, Ch. Kishore and PRIYA, R (2021) Analysis and Design of Data Mining Techniques to Develop Software Reliability. Journal of Advanced Research in Dynamical and Control Systems, 24 (4). pp. 167-173. ISSN 1943023X

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Abstract

- 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.

Item Type: Article
Subjects: Computer Science > Ethical Hacking
Divisions: Computer Science
Depositing User: Mr IR Admin
Date Deposited: 20 Sep 2024 08:54
Last Modified: 20 Sep 2024 08:54
URI: https://ir.vistas.ac.in/id/eprint/6708

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