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

Competition-based crowdsourcing software development: a multi-method study from a customer perspective

Crowdsourcing is emerging as an alternative outsourcing strategy which is gaining increasing attention in the software engineering community. However, crowdsourcing software development involves complex tasks which differ significantly from the micro-tasks that can be found on crowdsourcing platforms such as Amazon Mechanical Turk which are much shorter in duration, are typically very simple, and do not involve any task interdependencies. To achieve the potential benefits of crowdsourcing in the software development context, companies need to understand how this strategy works, and what factors might affect crowd participation. We present a multi-method qualitative and quantitative theory-building research study. Firstly, we derive a set of key concerns from the crowdsourcing literature as an initial analytical framework for an exploratory case study in a Fortune 500 company. We complement the case study findings with an analysis of 13,602 crowdsourcing competitions over a ten-year period on the very popular Topcoder crowdsourcing platform. Drawing from our empirical findings and the crowdsourcing literature, we propose a theoretical model of crowd interest and actual participation in crowdsourcing competitions. We evaluate this model using Structural Equation Modeling. Among the findings are that the level of prize and duration of competitions do not significantly increase crowd interest in competitions

The relationship between evolutionary coupling and defects in large industrial software (journal-first abstract)

In this study, we investigate the effect of EC on the defect-proneness of large industrial software systems and explain why the effects vary

Value of Big Data Analytics for Customs Supervision in e-Commerce

Big data and analytics have received a lot of attention in e-government research over the last decade and practitioners and researchers are looking into the transformative power of this technology to create for example competitive advantage, and increase transparency. Recent research points out that while parties are aware of the transformative power of this technology, understanding the value that this technology can bring for their specific organizations still remains a challenge. Data analytics is in particular interesting to support supervision tasks of governments. Here we take the customs supervision as a typical example where data analytics is used to support government in its supervision role. The main question addressed in this paper is: How to understand the value of big data analytics for government supervision? To address this question this research builds upon a case study where big data analytics solutions are developed and piloted as part of the PROFILE EU-funded research project. We adapt and utilize a recently published integrated model of big data value realization of Günther et al. [5] as a conceptual lens to structure the case findings. As a result we develop a more detailed model for analyzing value of big data specifically in the context of customs as an example of a specific domain of government supervision. This research contributes to the eGovernment literature on articulating value from big data analytics, particularly focusing on the role of government supervision.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Information and Communication Technolog