Software Engineering and Information Systems
Amin Moradbeiky; Amid Khatibi Bardsiri
Volume 4, Issue 1 , February 2018, , Pages 7-12
Abstract
Software project management has always faced challenges that have often had a great impact on the outcome of projects in future. For this, Managers of software projects always seek solutions against challenges. The implementation of unguaranteed approaches or mere personal experiences by managers does ...
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Software project management has always faced challenges that have often had a great impact on the outcome of projects in future. For this, Managers of software projects always seek solutions against challenges. The implementation of unguaranteed approaches or mere personal experiences by managers does not necessarily suffice for solving the problems. Therefore, the management area of software projects requires tools and means helping software project managers confront with challenges. The estimation of effort required for software development is among such important challenges. In this study, a neural-network-based architecture has been proposed that makes use of PSO algorithm to increase its accuracy in estimating software development effort. The architecture suggested here has been tested by several datasets. Furthermore, similar experiments were done on the datasets using various widely used methods in estimating software development. The results showed the accuracy of the proposed model. The results of this research have applications for researchers of software engineering and data mining.
Software Engineering and Information Systems
Ramin Saljoughinejad; Vahid Khatibi
Volume 4, Issue 1 , February 2018, , Pages 27-40
Abstract
The literature review shows software development projects often neither meet time deadlines, nor run within the allocated budgets. One common reason can be the inaccurate cost estimation process, although several approaches have been proposed in this field. Recent research studies suggest that in order ...
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The literature review shows software development projects often neither meet time deadlines, nor run within the allocated budgets. One common reason can be the inaccurate cost estimation process, although several approaches have been proposed in this field. Recent research studies suggest that in order to increase the accuracy of this process, estimation models have to be revised. The Constructive Cost Model (COCOMO) has often been referred as an efficient model for software cost estimation. The popularity of COCOMO is due to its flexibility; it can be used in different environments and it covers a variety of factors. In this paper, we aim to improve the accuracy of cost estimation process by enhancing COCOMO model. To this end, we analyze the cost drivers using meta-heuristic algorithms. In this method, the improvement of COCOMO is distinctly done by effective selection of coefficients and reconstruction of COCOMO. Three meta-heuristic optimization algorithms are applied synthetically to enhance the process of COCOMO model. Eventually, results of the proposed method are compared to COCOMO itself and other existing models. This comparison explicitly reveals the superiority of the proposed method.
Software Engineering and Information Systems
Mina Sadat Mousavi Kasravi; Mohammad Ahmadinia; Abbas Rezaiee
Volume 3, Issue 2 , May 2017, , Pages 65-74
Abstract
E-readiness is one of the major prerequisites for effective implementation of e-government. For the correct implementation of e-government, it is needed to accurately assess the state of e-readiness in desired community. In this regard, there are models to assess, but the correct choice of model is one ...
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E-readiness is one of the major prerequisites for effective implementation of e-government. For the correct implementation of e-government, it is needed to accurately assess the state of e-readiness in desired community. In this regard, there are models to assess, but the correct choice of model is one of the most important challenges in this area. The process of evaluating and selecting the appropriate options in the implementation of e-government due to the involvement of different groups of decision-makers, existence of interrelationships between technology and desired community as well as existing platforms is a complex process. In recent decades, with access to computational methods and powerful decision making systems selecting more accurate options, effective analysis of qualitative and quantitative characteristic and studying the interaction between them are provided. This article tries to examine the performance of e-readiness assessment models and multi criteria decision making methods and introduces the best selection of the e-readiness model for effective implementation of e-government. In order to reach this purpose, we introduced a layered architecture based on multi-criteria decision making methods and SWOT Analysis. The proposed layered architecture, reduces decision making errors and increases the accuracy in choosing the appropriate e-readiness assessment model.
Software Engineering and Information Systems
Vida Doranipour
Volume 3, Issue 2 , May 2017, , Pages 107-112
Abstract
Nowadays, effort estimation in software projects is turned to one of the key concerns for project managers. In fact, accurately estimating of essential effort to produce and improve a software product is effective in software projects success or fail, which is considered as a vital factor. Lack of access ...
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Nowadays, effort estimation in software projects is turned to one of the key concerns for project managers. In fact, accurately estimating of essential effort to produce and improve a software product is effective in software projects success or fail, which is considered as a vital factor. Lack of access to satisfying accuracy and little flexibility in existing estimation models have attracted the researchers’ attention to this area in last few years. One of the existing effort estimation methods is COCOMO (Constructive Cost Model) which has been taken importantly as an appropriate method for software projects. Although COCOMO has been invented some years ago, it has still got effort estimation ability in software projects. Many researchers have attempted to improve effort estimation ability in this model by improving COCOMO operation; but despite many efforts, COCOMO results are not satisfying yet. In this research, a new compound method is presented to increase COCOMO estimation accuracy. In the proposed method, much better factors are gained using combination of invasive weed optimization and COCOMO estimation method in contrast with basic COCOMO. With the best factors, the proposed model’s optimality will be maximized. In this method, a real data set is used for evaluating and its operation is analyzed in contrast to other models. Operational parameters improvement is affirmed by this model’s estimation results.
Software Engineering and Information Systems
Vahid Khatibi Bardsiri; Mahboubeh Dorosti
Volume 2, Issue 2 , May 2016, , Pages 11-22
Abstract
One of important aspects of software projects is estimating the cost and time required to develop projects. Nowadays, this issue has become one of the key concerns of project managers. Accurate estimation of essential effort to produce and develop software is heavily effective on success or failure of ...
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One of important aspects of software projects is estimating the cost and time required to develop projects. Nowadays, this issue has become one of the key concerns of project managers. Accurate estimation of essential effort to produce and develop software is heavily effective on success or failure of software projects and it is highly regarded as a vital factor. Failure to achieve convincing accuracy and little flexibility of current models in this field have attracted the attention of researchers in the last few years. Despite improvements to estimate effort, no agreement was obtained to select estimation model as the best one. One of effort estimation methods which is highly regarded is COCOMO. It is an extremely appropriate method to estimate effort. Although COCOMO was invented many years ago, it enjoys the effort estimation capability in software projects. Researchers have always attempted to improve the effort estimation capability in COCOMO through improving its structure. However, COCOMO results are not always satisfactory. The present study introduces a hybrid model for increasing the accuracy of COCOMO estimation. Combining bee colony algorithm with COCOMO estimation method, the proposed method obtained more efficient coefficient relative to the basic mode of COCOMO. Selecting the best coefficients maximizes the efficiency of the proposed method. The simulation results revealed the superiority of the proposed model based on MMRE and PRED(0.15).
Software Engineering and Information Systems
Behrouz Sadeghi; Vahid Khatibi Bardsiri; Monireh Esfandiari; Farzad Hosseinzadeh
Volume 1, Issue 4 , November 2015, , Pages 15-24
Abstract
One of the most important and valuable goal of software development life cycle is software cost estimation or SCE. During the recent years, SCE has attracted the attention of researchers due to huge amount of software project requests. There have been proposed so many models using heuristic and meta-heuristic ...
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One of the most important and valuable goal of software development life cycle is software cost estimation or SCE. During the recent years, SCE has attracted the attention of researchers due to huge amount of software project requests. There have been proposed so many models using heuristic and meta-heuristic algorithms to do machine learning process for SCE. COCOMO81 is one of the most popular models for SCE proposed by Barry Boehm in 1981. However COCOMO81 is an old estimation model, it has been widely used for the purpose of cost estimation in its new forms. In this paper, the Imperialism Competition Algorithm (ICA) has been employed to tune the COCOMO81 parameters. Experimental results show that in the separated COCOMO81 dataset, ICA can estimate the COCOMO81 model parameters such that the performance parameters are significantly improved. The proposed hybrid model is flexible enough to tune the parameters for any data sets in form of COCOMO81.