Exploring Organizations\u27 Software Quality Assurance Strategies

Poor software quality leads to lost profits and even loss of life. U.S. organizations lose billions of dollars annually because of poor software quality. The purpose of this multiple case study was to explore the strategies that quality assurance (QA) leaders in small software development organizations used for successful software quality assurance (SQA) processes. A case study provided the best research design to allow for the exploration of organizational and managerial processes. The target population group was the QA leaders of 3 small software development organizations who successfully implemented SQA processes, located in Saint John, New Brunswick, Canada. The conceptual framework that grounded this study was total quality management (TQM) established by Deming in 1980. Face-to-face semistructured interviews with 2 QA leaders from each organization and documentation including process and training materials provided all the data for analysis. NVivo software aided a qualitative analysis of all collected data using a process of disassembling the data into common codes, reassembling the data into themes, interpreting the meaning, and concluding the data. The resulting major themes were Agile practices, documentation, testing, and lost profits. The results were in contrast to the main themes discovered in the literature review, although there was some overlap. The implications for positive social change include the potential to provide QA leaders with the strategies to improve SQA processes, thereby allowing for improved profits, contributing to the organizations\u27 longevity in business, and strengthening the local economy

A Learning-based Method for Combining Testing Techniques

This work presents a method to combine testing techniques adaptively during the testing process. The method intends to mitigate the sources of uncertainty of software testing processes, by learning from past experience and, at the same time, adapting the technique selection to the current testing session. The method is based on machine learning strategies. Offline strategies are used to take into account historical information about techniques performance collected in past testing sessions; online strategies are used to adapt dynamically the selection of test cases to data observed as the testing proceeds. Experimental results show that the technique performance can be accurately characterized from features of testing sessions by means of machine learning algorithms, and that integrating this result into the online algorithm allows improving the fault detection effectiveness with respect to single testing techniques as well as to their random combination