Exploring the Applicability of Test Driven Development in the Big Data Domain

Big data analytics and the according applications have gained huge importance in daily life. This results on the one hand from their versatility and on the other hand from their capability to greatly improve an organization’s performance when utilized appropriately. However, despite their prevalence and the corresponding attention through practitioners as well as the scientific world, the actual implementation still remains a challenging task. Therefore, without the adequate testing, the reliability of the systems and thus the obtained outputs is uncertain. This might reduce their utilization, or even worse, lead to a diminished decision-making quality. The publication at hand explores the adoption of test driven development as a potential approach for addressing this issue. Subsequently, using the design science research methodology, a microservice-based test driven development concept for big data (MBTDD-BD) is proposed. In the end, possible avenues for future research endeavours are indicated

More Effective Ontology Authoring with Test-Driven Development and the TDDonto2 tool

Ontology authoring is a complex process, where commonly the automated reasoner is invoked for verification of newly introduced changes, therewith amounting to a time-consuming test-last approach. Test-Driven Development (TDD) for ontology authoring is a recent test-first approach that aims to reduce authoring time and increase authoring efficiency. Current TDD testing falls short on coverage of OWL features and possible test outcomes, the rigorous foundation thereof, and evaluations to ascertain its effectiveness. We aim to address these issues in one instantiation of TDD for ontology authoring. We first propose a succinct, logic-based specification of TDD testing and present novel TDD algorithms so as to cover also any OWL 2 class expression for the TBox and for the principal ABox assertions, and prove their correctness. The algorithms use methods from the OWL API directly such that reclassification is not necessary for test execution, therewith reducing ontology authoring time. The algorithms were implemented in TDDonto2, a Protégé plugin. TDDonto2 was evaluated by users, which demonstrated that modellers make significantly fewer errors with TDDonto2 compared to the standard Protégé interface and complete their tasks better using less time. Thus, the results indicate that TDD is a promising approach in an ontology development methodology