On the value of a prioritization scheme for resolving Self-admitted technical debt.
Abstract
Context: In addressing how best to estimate how much effort is required to develop software, a recent study found that using exemplary and recently completed projects [forming Bellwether moving windows (BMW)] in software effort prediction (SEP) models leads to relatively improved accuracy. More studies need to be conducted to determine whether the BMW yields improved accuracy in general, since different sizing and aging parameters of the BMW are known to affect accuracy. Objective: To investigate the existence of exemplary projects (Bellwethers) with defined window size and age parameters, and whether their use in SEP improves prediction accuracy. Method: We empirically investigate the moving window assumption based on the theory that the prediction outcome of a future event depends on the outcomes of prior events. Sampling of Bellwethers was undertaken using three introduced Bellwether methods (SSPM, SysSam, and RandSam). The ergodic Markov chain was used to determine the stationarity of the Bellwethers. Results: Empirical results show that 1) Bellwethers exist in SEP and 2) the BMW has an approximate size of 50 to 80 exemplary projects that should not be more than 2 years old relative to the new projects to be estimated. Conclusion: The study's results add further weight to the recommended use of Bellwethers for improved prediction accuracy in SEP. Index Terms-Bellwether effect, Bellwether moving window (BMW), growing portfolio (GP), Markov chains, software effort prediction (SEP). Manuscript received December 1, 2017; revised March 30, 2018; accepted May 10, 2018. Date of publication June 14, 2018; date of current version August 30, 2018. This work was supported in part by the General Research Fund of the Research Grants Council of Hong Kong under Grant 11208017 and Grant 11214116, and in part by the research funds of City University of Hong Kong under Grant 7004683, Grant 7004474, and Grant 9678149. Associate Editor: T. Dohi. (Corresponding authors: Solomon Mensah and Jacky Keung.) S. Mensah, J. Keung, and K. E. Bennin are with the Department of Computer Science, City University of Hong Kong, Hong Kong (e-mail:,smensah2-c@my. cityu.edu.hk; jacky.keung@city