Modeling Human Aspects to Enhance Software Quality Management

The aim of the research is to explore the impact of cognitive biases and social networks in testing and developing software. The research will aim to address two critical areas: i) to predict defective parts of the software, ii) to determine the right person to test the defective parts of the software. Every phase in software development requires analytical problem solving skills. Moreover, using everyday life heuristics instead of laws of logic and mathematics may affect quality of the software product in an undesirable manner. The proposed research aims to understand how mind works in solving problems. People also work in teams in software development that their social interactions in solving a problem may affect the quality of the product. The proposed research also aims to model the social network structure of testers and developers to understand their impact on software quality and defect prediction performance

Dione: An Integrated Measurement and Defect Prediction Solution

We present an integrated measurement and defect prediction tool: Dione. Our tool enables organizations to measure, monitor, and control product quality through learning based defect prediction. Similar existing tools either provide data collection and analytics, or work just as a prediction engine. Therefore, companies need to deal with multiple tools with incompatible interfaces in order to deploy a complete measurement and prediction solution. Dione provides a fully integrated solution where data extraction, defect prediction and reporting steps fit seamlessly. In this paper, we present the major functionality and architectural elements of Dione followed by an overview of our demonstration

Topic selection in industry experiments

This paper shares our experience with initial negotiation and topic elicitation process for conducting industry experiments in six software development organizations in Finland. The process involved interaction with company representatives in the form of both multiple group discussions and separate face-to-face meetings. Fitness criteria developed by researchers were applied to the list of generated topics to decide on a common topic. The challenges we faced include diversity of proposed topics, communication gaps, skepticism about research methods, initial disconnect between research and industry needs, and lack of prior work relationship. Lessons learned include having enough time to establish trust with partners, importance of leveraging the benefits of training and skill development that are inherent in the experimental approach, uniquely positioning the experimental approach within the landscape of other validation approaches more familiar to industrial partners, and introducing the fitness criteria early in the process