International Journal of Software Engineering and Knowledge Engineering

In today's software industry, the design of test cases is mostly based on human expertise, while test automation tools are limited to execution of pre-planned tests only. Evaluation of test outcomes is also associated with a considerable effort by human testers who often have imperfect knowledge of the requirements specification. Not surprisingly, this manual approach to software testing results in heavy losses to the world's economy. In this paper, we demonstrate the potential use of data mining algorithms for automated modeling of tested systems. The data mining models can be utilized for recovering system requirements, designing a minimal set of regression tests, and evaluating the correctness of software outputs. To study the feasibility of the proposed approach, we have applied a state-of-the-art data mining algorithm called Info-Fuzzy Network (IFN) to execution data of a complex mathematical package. The IFN method has shown a clear capability to identify faults in the tested program.

To have an effective and efficient mechanism to store, manage and utilize software sources is essential to the automation of software engineering. The paper presents an innovative approach in managing software resources using software warehouse where software assets are systematically accumulated, deposited, retrieved, packaged, managed and utilized, driven by data-mining and OLAP technologies. The results lead to streamlined high efficient software development process and enhance the productivity in response to modern challenges of the design and development of software applications.

Consider two independently done software engineering studies that used different approaches to cover some of the same subject area, such as software maintenance. Although done differently and for different purposes, to what extent can each study serve as a validation of the other? Within the scope of the subject area overlap, data mining can be applied to provide a quantitative assessment. This paper reports on the data mining that attempted to cross validate two independently done and published software engineering studies of software maintenance, one on a corrective maintenance maturity model, and the other on an objective classification of software maintenance activities. The data mining established that each of the two independently done studies effectively and very strongly validates the other.

This paper describes the application of two variants of clustering techniques to a matrix of requirements (use cases) vs. design units (classes), with the aim of augmenting the design of software component architectures. One algorithm used a modified COMO matrix-clustering algorithm, while the other is average-linkage agglomerative clustering based on similarity among classes as a measure of distance. Our results provide a shortcut for software engineers with limited experience in Component-Based Software Development (CBSD) who are constructing component architectures. This paper also evaluates the correlations between these two clustered results. We solicited feedback from experienced software developers who confirmed that the inferred hierarchies conformed to their conceptualizations of the software structure.

Identifying software component association is useful for component management and component retrieval. In this paper we design an evolutionary strategy to understand software structure better and identify software component association, by using genetic algorithm. Our mining strategy is effective for global search, especially when the searched space is so large that it is hardly possible to use deterministic search method.