Apostolos Ampatzoglou

George Digkas, Alexander Chatzigeorgiou, Apostolos Ampatzoglou et al.

While technical debt grows in absolute numbers as software systems evolve over time, the density of technical debt (technical debt divided by lines of code) is reduced in some cases. This can be explained by either the application of refactorings or the development of new artifacts with limited Technical Debt. In this paper we explore the second explanation, by investigating the relation between the amount of Technical Debt in new code and the evolution of Technical Debt in the system. To this end, we compare the Technical Debt Density of new code with existing code, and we investigate which of the three major types of code changes (additions, deletions and modifications) is primarily responsible for changes in the evolution of Technical Debt density. Furthermore, we study whether there is a relation between code quality practices and the 'cleanness' of new code. To obtain the required data, we have performed a large-scale case study on twenty-seven open-source software projects by the Apache Software Foundation, analyzing 66,661 classes and 56,890 commits. The results suggest that writing "clean" (or at least "cleaner") new code can be an efficient strategy for reducing Technical Debt Density, and thus preventing software decay over time. The findings also suggest that projects adopting an explicit policy for quality improvement, e.g. through discussions on code quality in board meetings, are associated with a higher frequency of cleaner new code commits. Therefore, we champion the establishment of processes that monitor the density of Technical Debt of new code to control the accumulation of Technical Debt in a software system.

Elvira-Maria Arvanitou, Apostolos Ampatzoglou, Alexander Chatzigeorgiou et al.

Background: The development of scientific software applications is far from trivial, due to the constant increase in the necessary complexity of these applications, their increasing size, and their need for intensive maintenance and reuse. Aim: To this end, developers of scientific software (who usually lack a formal computer science background) need to use appropriate software engineering (SE) practices. This paper describes the results of a systematic mapping study on the use of SE for scientific application development and their impact on software quality. Method: To achieve this goal we have performed a systematic mapping study on 359 papers. We first describe a catalogue of SE practices used in scientific software development. Then, we discuss the quality attributes of interest that drive the application of these practices, as well as tentative side-effects of applying the practices on qualities. Results: The main findings indicate that scientific software developers are focusing on practices that improve implementation productivity, such as code reuse, use of third-party libraries, and the application of "good" programming techniques. In addition, apart from the finding that performance is a key-driver for many of these applications, scientific software developers also find maintainability and productivity to be important. Conclusions: The results of the study are compared to existing literature, are interpreted under a software engineering prism, and various implications for researchers and practitioners are provided. One of the key findings of the study, which is considered as important for driving future research endeavors is the lack of evidence on the trade-offs that need to be made when applying a software practice, i.e., negative (indirect) effects on other quality attributes.