The use of data Mining Techniques for Improving Software Reliability
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Abstract
The primary goal of software development is to deliver high-quality software efficiently and in the least amount of time whenever
possible. To achieve the preceding goal, developers often want to reuse existing frameworks or libraries instead of developing similar code artifacts
from scratch. The challenging aspect for developers in reusing the existing frameworks or libraries is to understand the usage patterns and
ordering rules among Application Programming Interfaces (APIs) exposed by those frameworks or libraries, because many of the existing
frameworks or libraries are not well documented. Incorrect usage of APIs may lead to violated API specifications, leading to security and robustness
defects in the software. Furthermore, usage patterns and specifications might change with library refactorings, requiring changes in the
software that reuse the library.
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.
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