Quality in the evolution of mobile applications

Mobile applications are becoming complex software systems as they rapidly evolve and grow constantly to meet user requirements. However, satisfying these requirements may lead to poor design choices known as 'antipatterns' that can degrade software quality and performance. Therefore, perception and monitoring of the characteristics of mobile applications are important activities to facilitate maintenance and development, so that developers are directed to restructure their practices and upgrade their qualifications. The quality of mobile applications is of great importance for developers, users and application stores. Although there are a number of mobile application specific techniques for analysing the quality of a mobile software product, there is no accepted and valid way to predict the potential success of a mobile application in a real app store. Thus, the monitoring of the quality of ever-evolving mobile software throughout the evolution has become an attractive problem to investigate. In this thesis, we aim to monitor the quality in the evolution of open source mobile applications in the light of existing studies in the literature. For this purpose, the development of internal (code-based) and external quality (community-based) features of open source mobile applications has been evaluated with an exploratory approach. For code-based evaluation, object-based design metrics are measured based on the most recent quality model ISO/IEC 25010. For community-based evaluation, a number of community based metrics which are extracted from data repositories such as Github and Sourceforge have been analyzed based on the DeLone and McLean model, which is a guideline for measuring the success of open source software. As a result of the analysis, it was observed that the internal quality increased generally during the releases, while the external quality decreased. In addition, the relationship between internal and external qualities of the applications was analyzed by Spearman correlation analysis and no significant relation was observed between them.