Inferencing Relational Database Tuning Actions with OnDBTuning Ontology

OnDBTuning is a relational database (automatic) tuning ontology. Ontologies are software artifacts that represent specific domain knowledge and can infer new knowledge. However, most cases involve only a formal and static description of concepts. Moreover, as database tuning involves many rules-ofthumb and black-box algorithms, it becomes challenging to describe these inference procedures. This research work first presents the OnDBTuning ontology solution focusing on the inference of tuning actions. Next, we provide an actual implementation using SPARQL Inferencing Notation (SPIN). Finally, we discuss a practical evaluation for index recommendation.

Using git metrics to measure students' and teams' code contributions in software development projects

Many software engineering courses are centered around team-based project development. Analyzing the source code contributions during the projects’ development could provide both instructors and students with constant feedback to identify common trends and behaviors that can be improved during the courses. Evaluating course projects is a challenge due to the difficulty of measuring individual student contributions versus team contributions during the development. The adoption of distributed version control sys-tems like git enable the measurement of students’ and teams’ contributions to the project.In this work, we analyze the contributions within eight software development projects,with 150 students in total, from undergraduate courses that used project-based learning.We generate visualizations of aggregated git metrics using inequality measures and the contribution per module, which offer insights into the practices and processes followed by students and teams throughout the project development. This approach allowed us to identify inequality among students’ contributions, the modules where students con-tributed, development processes with a non-steady pace, and integration practices render-ing a useful feedback tool for instructors and students during the project’s development.Further studies can be conducted to assess the quality, complexity, and ownership of the contributions by analyzing software artifacts.

Weighted software metrics aggregation and its application to defect prediction

It is a well-known practice in software engineering to aggregate software metrics to assess software artifacts for various purposes, such as their maintainability or their proneness to contain bugs. For different purposes, different metrics might be relevant. However, weighting these software metrics according to their contribution to the respective purpose is a challenging task. Manual approaches based on experts do not scale with the number of metrics. Also, experts get confused if the metrics are not independent, which is rarely the case. Automated approaches based on supervised learning require reliable and generalizable training data, a ground truth, which is rarely available. We propose an automated approach to weighted metrics aggregation that is based on unsupervised learning. It sets metrics scores and their weights based on probability theory and aggregates them. To evaluate the effectiveness, we conducted two empirical studies on defect prediction, one on ca. 200 000 code changes, and another ca. 5 000 software classes. The results show that our approach can be used as an agnostic unsupervised predictor in the absence of a ground truth.

Evolution of existing software to mobile computing platforms: Framework support and case study

Mobile computing as ubiquitous and pervasive technology supports portable and context-aware computation. To date, there exist a significant number of traditional computing systems–running on the web and/or workstation-based platforms–that lack features of mobile computing, including but not limited to ubiquity, context-sensing, and high interactivity. Software that executes on these traditional computing systems is referred to as legacy software that can be upgraded to exploit the features of mobile technologies. However, legacy software may contain critical data, logic, and processes that cannot be easily replaced. One of the solutions is to evolve legacy software systems by (a) upgrading their functionality while (b) preserving their data and logic. Recently research and development efforts are focused on modernizing the legacy systems as per the needs of service and cloud-based platforms. However, there does not exist any research that supports a systematic modernization of legacy software as per the requirements of the mobile platforms. We propose a framework named Legacy-to-Mobile as a solution that supports an incremental and process-driven evolution of the legacy software to mobile computing software. The proposed Legacy-to-Mobile framework unifies the concepts of software reverse engineering (recovering software artifacts) and software change (upgrading software artifacts) to support the legacy evolution. The framework follows an incremental approach with four processes that include (i) evolution planning, (ii) architecture modeling, (iii) architecture change, and (iv) software validation of mobile computing software. The framework provides the foundation (as part of futuristic research) to develop a tool prototype that supports automation and user decision support for incremental and process-driven evolution of legacy software to mobile computing platforms.