Closing the Defect Reduction Gap between Software Inspection and Test-Driven Development: Applying Mutation Analysis to Iterative, Test-First Programming

Abstract

The main objective of this dissertation is to assist in reducing the chaotic state of the software engineering discipline by providing insights into both the effectiveness of software defect reduction methods and ways these methods can be improved. The dissertation is divided into two main parts. The first is a quasi-experiment comparing the software defect rates and initial development costs of two methods of software defect reduction: software inspection and test-driven development (TDD). Participants, consisting of computer science students at the University of Arizona, were divided into four treatment groups and were asked to complete the same programming assignment using either TDD, software inspection, both, or neither. Resulting defect counts and initial development costs were compared across groups. The study found that software inspection is more effective than TDD at reducing defects, but that it also has a higher initial cost of development. The study establishes the existence of a defect-reduction gap between software inspection and TDD and highlights the need to improve TDD because of its other benefits.The second part of the dissertation explores a method of applying mutation analysis to TDD to reduce the defect reduction gap between the two methods and to make TDD more reliable and predictable. A new change impact analysis algorithm (CHA-AS) based on CHA is presented and evaluated for applications of software change impact analysis where a predetermined set of program entry points is not available or is not known. An estimated average case complexity analysis indicates that the algorithm's time and space complexity is linear in the size of the program under analysis, and a simulation experiment indicates that the algorithm can capitalize on the iterative nature of TDD to produce a cost savings in mutation analysis applied to TDD projects. The algorithm should also be useful for other change impact analysis situations with undefined program entry points such as code library and framework development.An enhanced TDD method is proposed that incorporates mutation analysis, and a set of future research directions are proposed for developing tools to support mutation analysis enhanced TDD and to continue to improve the TDD method.