Due to the ongoing trend of digitalization, the importance of software for today’s society is continuously increasing. Naturally, there is also a huge interest in improving its quality, which led to a highly active research community dedicated to this aim. Consequently, a plethora of propositions, tools, and methods emerged from the corresponding efforts. One of the approaches that have become highly prominent is the concept of test-driven development (TDD) that increases the quality of created software by restructuring the development process. However, such a big change to the followed procedures is usually also accompanied by major challenges that pose a risk for the achievement of the set targets. In order to find ways to overcome them, or at least to mitigate their impact, it is necessary to identify them and to subsequently raise awareness. Furthermore, since the effect of TDD on productivity and quality is already extensively researched, this work focuses only on issues besides these aspects. For this purpose, a literature review is presented that focuses on the challenges of TDD. In doing so, challenges that can be attributed to the three categories of people, software, and process are identified and potential avenues for future research are discussed.
Big data attracts researchers and practitioners around the globe in their desire to effectively manage the data deluge resulting from the ongoing evolution of the information systems domain. Consequently, many decision makers attempt to harness the potentials arising with the use of those modern technologies in a multitude of application scenarios. As a result, big data has gained an important role for many businesses. However, as of today, the developed solutions are oftentimes perceived as completed products, without considering that the application in highly dynamic environments might benefit from a deviation of this approach. Relevant data sources as well as the questions that are supposed to be answered by their analysis may change rapidly and so do subsequently the requirements regarding the functionalities of the system. To our knowledge, while big data itself is a prominent topic, fields of application that are likely to evolve in a short period of time and the resulting consequences were not specifically investigated until now. Therefore, this research aims to overcome this paucity by clarifying the relation between dynamic business environments and big data analytics (BDA), sensitizing researchers and practitioners for future big data engineering activities. Apart from a thorough literature review, expert interviews are conducted that evaluate the made inferences regarding dynamic and stable influencing factors, the influence of dynamic environments on BDA applications as well as possible countermeasures. The ascertained insights are condensed into a proposal for decision making, facilitating the alignment of BDA and business needs in dynamic business environments.
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