Fahmid Morshed Fahid

N. C. Shrikanth, William Nichols, Fahmid Morshed Fahid et al.

Software engineering is a highly dynamic discipline. Hence, as times change, so too might our beliefs about core processes in this field. This paper checks some five beliefs that originated in the past decades that comment on the relationships between (i) developer productivity; (ii) software quality and (iii) years of developer experience. Using data collected from 1,356 developers in the period 1995 to 2006, we found support for only one of the five beliefs titled "Quality entails productivity". We found no clear support for four other beliefs based on programming languages and software developers. However, from the sporadic evidence of the four other beliefs we learned that a narrow scope could delude practitioners in misinterpreting certain effects to hold in their day to day work. Lastly, through an aggregated view of assessing the five beliefs, we find programming languages act as a confounding factor for developer productivity and software quality. Thus the overall message of this work is that it is both important and possible to revisit old beliefs in SE. Researchers and practitioners should routinely retest old beliefs.

Zhe Yu, Fahmid Morshed Fahid, Huy Tu et al.

Keeping track of and managing Self-Admitted Technical Debts (SATDs) are important to maintaining a healthy software project. This requires much time and effort from human experts to identify the SATDs manually. The current automated solutions do not have satisfactory precision and recall in identifying SATDs to fully automate the process. To solve the above problems, we propose a two-step framework called Jitterbug for identifying SATDs. Jitterbug first identifies the "easy to find" SATDs automatically with close to 100% precision using a novel pattern recognition technique. Subsequently, machine learning techniques are applied to assist human experts in manually identifying the remaining "hard to find" SATDs with reduced human effort. Our simulation studies on ten software projects show that Jitterbug can identify SATDs more efficiently (with less human effort) than the prior state-of-the-art methods.