Predicting Time Range of Development Based on Generalized Software Reliability Model, accepted at APSEC 2014 (CORE Rank B, acceptance rate 67/226=30%).

Kiyoshi Honda, Hidenori Nakai, Hironori Washizaki, Yoshiaki Fukazawa (Waseda University), Ken Asoh, Kaz Takahashi, Kentarou Ogawa, Maki Mori, Takashi Hino, Yosuke Hayakawa, Yasuyuki Tanaka, Shinichi Yamada, Daisuke Miyazaki (Yahoo Japan Corporation), “Predicting Time Range of Development Based on Generalized Software Reliability Model,” 21st Asia-Pacific Software Engineering Conference (APSEC 2014), Jeju, Korea, December 1-4, 2014. (to appear) (CORE Rank B, acceptance rate 67/226=30%)

Development environments have changed drastically, development periods are shorter than ever and the number of team members has increased. These changes have led to difficulties in controlling the development activities and predicting when the development will end. Especially, quality managers try to control software reliability and project managers try to estimate the end of development for planing developing term and distribute the manpower to other developments. In order to assess recent software developments, we propose a generalized software reliability model (GSRM) based on a stochastic process, and simulate developments that include uncertainties and dynamics. We also compare our simulation results to those of other software reliability models. Using the values of uncertainties and dynamics obtained from GSRM, we can evaluate the developments in a quantitative manner. Additionally, we use equations to define the uncertainty regarding the time required to complete a development, and predict whether or not a development will be completed on time. We compare GSRM with an existing model using two old actual datasets and one new actual dataset which we collected, and show that the approximation curve generated by GSRM is about 12% more precise than that generated by the existing model. Furthermore, GSRM can narrow down the predicted time range in which a development will end to less than 40% of that obtained by the existing model.