A STUDY ON HUMANIZING SOFTWARE TEST EFFORT AND QUALITY
N. Srinivasan · 2015
For improving software development processes with the goal of developing high-quality software within budget and planned cycle time, Capability Maturity Model (CMM) has become a popular methodology. Prior investigation focusing on CMM level 5 projects, has identified many factors as determinants of software development effort, quality, and cycle time. Using a linear regression model based on data collected from different CMM level 5 projects of reputed organizations, that high levels of process maturity, as indicated by CMM level 5 rating, reduce the effects of most factors that were previously believed to impact software development effort, quality, and cycle time were found. The only factor found to be significant in determining effort, cycle time, and quality was software size. Testing is more than just debugging. The purpose of testing can be quality assurance, verification and validation, or reliability estimation. Particularly regression testing is an expensive, but important, process. Unfortunately, there may be insufficient resources to allow for the re execution of all test cases during regression testing. In this situation, test cases are needed to be prioritized. Regression testing improves the effectiveness of regression by ordering the test cases so that the most beneficial are executed first. There are many studies on regression test case prioritization which mainly has focuses on Greedy Algorithms(GA). However, it is known that these algorithms may produce suboptimal results because they may construct results that denote only local minima within the search space. By contrast, meta heuristic and evolutionary search algorithms aim to avoid such problems. This paper addresses the problems of choice of fitness metric, characterization of landscape modality and determination of the most suitable search technique to apply. The empirical results replicate previous results concerning GA. The results show that GA perform well, although Greedy approaches are surprisingly effective given the multimodal nature of the landscape.