No abstract

No abstract

Abstract-Automated testing is a basic principle of agile development. Its benefits include early defect detection, defect cause localization and removal of fear to apply changes to the code. Therefore, maintaining high quality test code is essential. This study introduces a model that assesses test code quality by combining source code metrics that reflect three main aspects of test code quality: completeness, effectiveness and maintainability. The model is inspired by the Software Quality Model of the Software Improvement Group which aggregates source code metrics into quality ratings based on benchmarking. To validate the model we assess the relation between test code quality, as measured by the model, and issue handling performance. An experiment is conducted in which the test code quality model is applied to 18 open source systems. The test quality ratings are tested for correlation with issue handling indicators, which are obtained by mining issue repositories. In particular, we study the (1) defect resolution speed, (2) throughput and (3) productivity issue handling metrics. The results reveal a significant positive correlation between test code quality and two out of the three issue handling metrics (throughput and productivity), indicating that good test code quality positively influences issue handling performance.

The UML (Unified Modeling Language) has become the de facto standard for software modeling in the software industry. Despite its wide acceptance, little is known about how UML is used in practice, let alone the challenges and difficulties faced by engineers who work with this modeling notation. In this paper, we provide empirical findings from a survey on the use of UML amongst 80 professional software engineers. We explore software engineers' opinions on common styles of using UML and how they perceive the impact of using UML on productivity and quality in software development. One of the results reveals that the impact of using the UML on productivity is perceived mostly in the design, analysis, and implementation phases.

No abstract

No abstract

Abstract. The contribution of formal modeling approaches in software development has always been a subject of debates. The proponents of model-driven development argue that big upfront designs although require substantial investment will payoff later in the implementation phase in terms of increased productivity and quality. On the other hand, software engineers who are not very keen on modeling perceive the activity as simply a waste of time and money without any real contribution to the final software product. Considering present advancement of modelbased software development in software industry, we are challenged to investigate the real contribution of modeling in software development. Therefore, in this paper we report on an empirical investigation on the impact of UML modeling on the quality of software system. In particular, we focus on defect density as a measure of software quality. Based on a significant industrial case study, we have found that the use of UML modeling potentially reduces defect density in software system.

Context: Research indicates that software quality, to a large extent, depends on cooperation within software teams [1]. Since software development is a creative process that involves human interaction in the context of a team, it is important to understand the teamwork factors that influence performance.Objective: We present a study design in which we aim to examine the factors within software development teams that have significant influence on the performance of the team. We propose to consider factors such as communication, coordination of expertise, cohesion, trust, cooperation, and value diversity. The study investigates whether and to which extent these factors correlate with a performance of the team. In order to capture a variety of relevant teamwork factors, we created a new model extending the work of Hoegl and Gemuenden [2] and Liang et al. [3]. Method:The study is based on quantitative research by means of an online questionnaire. We invited more than 20 software development teams in the Netherlands to participate in our team performance assessment, evaluating the teamwork and performance of the team. Based on an average team size of five people, one would therefore expect at least 100 participants in total. Also, product stakeholders will be asked to give their independent assessments of the performance of the team.Expected result: By analyzing the correlation between teamwork factors and team performance, we expect to gain a deeper understanding of how teamwork factors influence team performance. We also expect to validate the implemented extensions of teamwork model with respect to earlier work.