Exploiting the architectural characteristics of software components to improve software reuse

PhD ThesisSoftware development is a costly process for all but the most trivial systems. One of the commonly known ways of minimizing development costs is to re-use previously built software components. However, a significant problem that source-code re-users encounter is the difficulty of finding components that not only provide the functionality they need but also conform to the architecture of the system they are building. To facilitate finding reusable components there is a need to establish an appropriate mechanism for matching the key architectural characteristics of the available source-code components against the characteristics of the system being built. This research develops a precise characterization of the architectural characteristics of source-code components, and investigates a new way to describe how appropriate components for re-use can be identified and categorized.Umm Al- Qura University

An experimental study of a fuzzy adaptive emperor penguin optimizer for global optimization problem

Emperor Penguin Optimizer (EPO) is a recently developed population-based meta-heuristic algorithm that simulates the huddling behavior of emperor penguins. Mixed results have been observed on the performance of EPO in solving general optimization problems. Within the EPO, two parameters need to be tuned (namely f and l ) to ensure a good balance between exploration (i.e., roaming unknown locations) and exploitation (i.e., manipulating the current known best). Since the search contour varies depending on the optimization problem, the tuning of f and l is problem-dependent, and there is no one-size-fits-all approach. To alleviate these problems, an adaptive mechanism can be introduced in EPO. This paper proposes a fuzzy adaptive variant of EPO, namely Fuzzy Adaptive Emperor Penguin Optimizer (FAEPO), to solve this problem. As the name suggests, FAEPO can adaptively tune the parameters f and l throughout the search based on three measures (i.e., quality, success rate, and diversity of the current search) via fuzzy decisions. A test suite of twelve optimization benchmark test functions and three global optimization problems (Team Formation Optimization - TFO, Low Autocorrelation Binary Sequence - LABS, and Modified Condition/Decision Coverage - MC/DC test case generation) were solved using the proposed algorithm. The respective solution results of the benchmark meta-heuristic algorithms were compared. The experimental results demonstrate that FAEPO significantly improved the performance of its predecessor (EPO) and gives superior performance against the competing meta-heuristic algorithms, including an improved variant of EPO (IEPO)