Abstract—Software developers around the globe are actively asking a question(s) and sharing solutions to the problems related to software development on Stack Overflow - a social question and answer (Q&A) website. The knowledge shared by software developers on Stack Overflow contains useful information related to software development such as feature requests (functional/non-functional), code snippets, reporting bugs or sentiments. How to extract the functional and nonfunctional requirements shared by mobile application developers on social/programming Q&A website Stack Overflow has become a challenge and a less researched area. To understand the problems, needs, and trend in the iOS mobile application development, we evaluated the quality requirements or non-functional requirements (NFRs) on Stack Overflow posts. To this end, we applied Latent Dirichlet Allocation (LDA) topic models, to identify the main topics in iOS posts on Stack Overflow. Besides, we labeled the extracted topics with quality requirements or NFRs by using the wordlists to evaluate the trend, evolution, hot and unresolved NFRS in all iOS discussions. Our findings revealed that the highly frequent topics the iOS developers discussed are related to usability, reliability, and functionality followed by efficiency. Interestingly, the most problematic areas unresolved are also usability, reliability, and functionality though followed by portability. Besides, the evolution trend of each of the six different quality requirements or NFRs over time is depicted through comprehensive visualization.
Index Terms—Non-functional requirements (NFRs), quality requirements, iOS, latent dirichlet allocation (LDA), stack overflow.
The authors are with School of Computer Science & Technology, Beijing Institute of Technology, Beijing, 100081, China (e-mail: email@example.com, firstname.lastname@example.org).
Cite: Arshad Ahmad, Kan Li, Chong Feng, and Tingting Sun, "An Empirical Study on How iOS Developers Report Quality Aspects on Stack Overflow," International Journal of Machine Learning and Computing vol. 8, no. 5, pp. 501-506, 2018.