A Review of Metrics and Modeling Techniques in Software Fault Prediction Model Development

This paper surveys different software fault predictions progressed through different data analytic techniques reported in the software engineering literature. This study split in three broad areas; (a) The description of software metrics suites reported and validated in the literature. (b) A brief outline of previous research published in the development of software fault prediction model based on various analytic techniques. This utilizes the taxonomy of analytic techniques while summarizing published research. (c) A review of the advantages of using the combination of metrics. Though, this area is comparatively new and needs more research efforts

A Baseline Method For Search-Based Software Engineering

Associated research group: Critical Systems Research GroupBackground: Search-based Software Engineering (SBSE) uses a variety of techniques such as evolutionary algorithms or meta-heuristic searches but lacks a standard baseline method. Aims: The KEYS2 algorithm meets the criteria of a baseline. It is fast, stable, easy to understand, and presents results that are competitive with standard techniques. Method: KEYS2 operates on the theory that a small sub-set of variables control the majority of the search space. It uses a greedy search and a Bayesian ranking heuristic to fix the values of these variables, which rapidly forces the search towards stable high-scoring areas. Results: KEYS2 is faster than standard techniques, presents competitive results (assessed with a rank-sum test), and offers stable solutions. Conclusions: KEYS2 is a valid candidate to serve as a baseline technique for SBSE research