Selecting Best Software Reliability Growth Models: A Social Spider Algorithm based Approach

Abstract

Software Reliability is considered to be an essential part of software systems; it involves measuring the system's probability of having failures; therefore, it is strongly related to Software Quality. Software Reliability Growth Models are used to indicate the expected number of failures encountered after the software has been completed, it is also an indicator of the software readiness to be delivered. This paper presents a study of selecting the best Software Reliability Growth Model according to the dataset at hand. Several Comparison Criteria are used to yield a ranking methodology to be used in pointing out best models. The Social Spider Algorithm (SSA), one of the newly introduced Swarm Intelligent Algorithms, is used for estimating the parameters of the SRGMs for two datasets. Results indicate that the use of SSA was efficient in assisting the process of criteria weighting to find the optimal model and the best overall ranking of employed models.

Novel methodology for the geophysical interpretation of magnetic anomalies due to simple geometrical bodies using social spider optimization (SSO) algorithm

“…Since the direction of movement of spiders (towards or away) from each other depends mainly on neighboring spider's vibration, the movement was modeled using Eq. ( 12) (Akram & Abd-AlKareem, 2018;Ben et al, 2021b;Bhandari et al, 2018) as where, α, β, δ and r are random numbers within the range of 0 and 1, k is the total number of maximum iterations, TV is the threshold value otherwise known as the probability factor, and s c and s b are the nearest best spider to spider i, and the best spider in the entire population S, respectively.…”

“…Examples of such defects include exploration-exploitation imbalances and suboptimal solutions from premature convergences. This computational edge has motivated the suc- cessful application of SSO in diverse fields of engineering (Husodo et al, 2020;Arul Xavier and Annadurai, 2019;Shayanfar et al, 2016), image processing (Cuevas et al, 2018;Bhandari et al, 2018;Akram & Abd-AlKareem, 2018;Ouadfel and Taleb-Ahmed, 2016) and energy (Alrashidi et al, 2020;Ewees et al, 2017) amongst others. Records of applications of SSO procedure in these fields of research, increasingly promise the algorithm promises to be a meaningful and appropriate inversion tool for geophysical data inversion.…”