In this work, the antlion optimization (ALO) is employed due to its efficiency and wide applicability to estimate the parameters of four modified models of the basic constructive cost model (COCOMO) model. Three tests are carried out to show the effectiveness of ALO: first, it is used with Bailey and Basili dataset for the basic COCOMO Model and Sheta's Model 1 and 2, and is compared with the firefly algorithm (FA), genetic algorithms (GA), and particle swarm optimization (PSO). Second, parameters of Sheta's Model 1 and 2, Uysal's Model 1 and 2 are optimized using Bailey and Basili dataset; results are compared with directed artificial bee colony algorithm (DABCA), GA, and simulated annealing (SA). Third, ALO is used with Basic COCOMO model and four large datasets, results are compared with hybrid bat inspired gravitational search algorithm (hBATGSA), improved BAT (IBAT), and BAT algorithms. Results of Test1 and Test2 show that ALO outperformed others, as for Test3, ALO is better than BAT and IBAT using MAE and the number of best estimations. ALO proofed achieving better results than hBATGSA for datasets 2 and 4 out of the four datasets explored in terms of MAE and the number of best estimates.
In the software projects, one of the essential and difficult problems faced by the managers in the competitive software industry is the Software Project Scheduling Problem (SPSP). With the increase in employees and tasks’ numbers the problem is becoming an NP-hard problem. The goal of this proposal is to resolve the problem of the software project scheduling with Whale optimization algorithm (WOA) and utilized it on various instances from three datasets. In order to prove the soundness and viability of Whale optimization algorithm (WOA), we illustrated some experimental results. This algorithm gave good outcomes for datasets that have a few tasks but failed to find feasible solutions when increasing the number of tasks.
In this paper, we will explore the application of grey wolf optimization (GWO) methodology in order to solve the software project scheduling problem (SPSP) to seek an optimum solution via applying different instances from two datasets. We will focus on the effects of the quantity of employees as well as the number of tasks which will be accomplished. We concluded that increasing employee number will decrease the project's duration, but we could not find any explanation for the cost values for all instances that studied. Also, we concluded that, when increasing the number of the tasks, both the cost and duration will be increased. The results will compare with a max-min ant system hyper cube framework (MMAS-HC), intelligent water drops algorithm (IWD), firefly algorithm (FA), ant colony optimization (ACO), intelligent water drop algorithm standard version (IWDSTD), and intelligent water drop autonomous search (IWDAS). According to these study and comparisons, we would like to say that GWO algorithm is a better optimizing tool for all instances, except one instance that FA is outperform the GWO.
Software design is one of the very important phases of the software engineering. The costs of software can be minimized if improvements or corrections made during this stage. Several of the current computer aided software engineering (CASE) tools like enterprise architect (EA) v12 do not have the capability to improve the design. This work aims to develop an algorithm that helps the software engineers evaluating the design quality utilizing one of the object-oriented (OO) design models namely quality metrics for object-oriented design (QMOOD) which represents as hierarchical model that describes the relationship between quality attributes such as reusability, extendibility and properties of the design of OO design. This algorithm describesed how the assessment of the extendibility/ extensibility using the software metrics has been done and the impact of the involved metrics in the extendibility value. Results obtained demonstrate the effect of OO design metrics such as inheritance, polymorphism, abstraction and coupling in quality characteristics like extensibility. The results show that lower values of abstraction and coupling, obtain higher value of extendibility which means the class diagram is ready to accept additional improvements. The proposed algorithm has been tested on two different systems (test cases) that vary in their class diagrams, functionalities, and complexities.
The process of finding a function that can estimate the effort of software systems is considered to be the most important and most complex process facing systems developers in the field of software engineering. The accuracy of estimating software effort forms an essential part of the software development phases. A lot of experts applied different ways to find solutions to this issue, such as the COCOMO and other methods. Recently, many questions have been put forward about the possibility of using Artificial Intelligence to solve such problems, different scientists made several studies about the use of techniques such as Genetic Algorithms and Artificial Neural Networks to solve estimation problems. This work utilizes one of the Linear Genetic Programming methods (Multi Expression programming) which apply the principle of competition between equations encrypted within the chromosomes to find the best formula for resolving the issue of software effort estimation. As for to the test data, benchmark known datasets are employed taken from previous projects, the results are evaluated by comparing them with the results of Genetic Programming (GP) using different fitness functions. The gained results indicate the surpassing of the employed method in finding more efficient functions for estimating about 7 datasets each consisting of many projects.
The maintainability of the software is one of the most substantial aspects when assessing software product quality. It is known as the easiness with which the current software can be changed. In the literature, a great number of models have been suggested to predict and measure maintainability during various stages of the Software Development Life Cycle, to conduct a comparative study of the existing suggested models of the prediction, only few attempts have been done. This study hints at the basics about the manner of how to measure maintainability in the object-oriented (OO) design knowing that the maintainability will be measured differently at every level. Also, we will concentrate on the artificial intelligence technologies of these studies.
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