فهرس المقالات effort estimation

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  1 - An Improved COCOMO based Model to Estimate the Effort of Software Projects
  Vahid Khatibi Bardsiri Mahboubeh Dorosti
  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 ef أکثر

  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). تفاصيل المقالة
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  2 - Improvement of effort estimation accuracy in software projects using a feature selection approach
  Zahra Shahpar Vahid Khatibi Asma Tanavar Rahil Sarikhani
  In recent years, utilization of feature selection techniques has become an essential requirement for processing and model construction in different scientific areas. In the field of software project effort estimation, the need to apply dimensionality reduction and featu أکثر

  In recent years, utilization of feature selection techniques has become an essential requirement for processing and model construction in different scientific areas. In the field of software project effort estimation, the need to apply dimensionality reduction and feature selection methods has become an inevitable demand. The high volumes of data, costs, and time necessary for gathering data , and also the complexity of the models used for effort estimation are all reasons to use the methods mentioned. Therefore, in this article, a genetic algorithm has been used for feature selection in the field of software project effort estimation. This technique has been tested on well-known data sets. Implementation results indicate that the resulting subset, compared to the original data set, has produced better outcomes in terms of effort estimation accuracy. This article showed that genetic algorithms are ideal methods for selecting a subset of features and improving effort estimation accuracy. تفاصيل المقالة
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  3 - Type-2 Fuzzy Logic Approach To Increase The Accuracy Of Software Development Effort Estimation
  Zahra Barati Mahdi Jafari Shahbazzadeh Vahid Khatibi Bardsiri
  predicting the effort of a successful project has been a major problem for software engineers the significance of which has led to extensive investigation in this area. One of the main objectives of software engineering society is the development of useful models to pre أکثر

  predicting the effort of a successful project has been a major problem for software engineers the significance of which has led to extensive investigation in this area. One of the main objectives of software engineering society is the development of useful models to predict the costs of software product development. The absence of these activities before starting the project will lead to various problems. Researchers focus their attention on determining techniques with the highest effort prediction accuracy or on suggesting new combinatory techniques for providing better estimates. Despite providing various methods for the estimation of effort in software projects, compatibility and accuracy of the existing methods is not yet satisfactory. In this article, a new method has been presented in order to increase the accuracy of effort estimation. This model is based on the type-2 fuzzy logic in which the gradient descend algorithm and the neuro-fuzzy-genetic hybrid approach have been used in order to teach the type-2 fuzzy system. In order to evaluate the proposed algorithm, three databases have been used. The results of the proposed model have been compared with neuro-fuzzy and type-1 fuzzy system. This comparison reveals that the results of the proposed model have been more favorable than those of the other two models. تفاصيل المقالة
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  4 - A New Architecture Based on Artificial Neural Network and PSO Algorithm for Estimating Software Development Effort
  Amin Moradbeiky Amid Khatibi Bardsiri
  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 perso أکثر

  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. تفاصيل المقالة
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  5 - A New Optimized Hybrid Model Based On COCOMO to Increase the Accuracy of Software Cost Estimation
  Ramin Saljoughinejad Vahid Khatibi
  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 resea أکثر

  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. تفاصيل المقالة
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  6 - مقایسه روش های طبقه بندی در تخمین تلاش توسعه نرم افزار
  صادق انصاری پور تقی جاودانی گندمانی
  نادرست بودن تخمین هزینه نرم افزار یکی از دلایل مهم ناامیدی متخصصان نرم افزار و محققان تخمین هزینه بوده است و علیرغم تلاش های فراوانی که برای بهبود آن انجام شده است اما هنوز هم دقت تخمین پایین است. عدم تجزیه و تحلیل مناسب در ابتدای شروع به کار پروژه و همچنین عدم به روز آ أکثر

  نادرست بودن تخمین هزینه نرم افزار یکی از دلایل مهم ناامیدی متخصصان نرم افزار و محققان تخمین هزینه بوده است و علیرغم تلاش های فراوانی که برای بهبود آن انجام شده است اما هنوز هم دقت تخمین پایین است. عدم تجزیه و تحلیل مناسب در ابتدای شروع به کار پروژه و همچنین عدم به روز آن در حین انجام پروژه یکی از مهم ترین دلایل شکست پروژه ها محسوب می شود. اگر چه زمانی که یک پروژه‌ها بسته می‌شوند، بازخورد های آن ایجاد می‌شود، اما اگر تخمین‌ها و واقعیات ثبت‌شده با پروژه انجام‌شده به طور کامل مطابقت نداشته باشند، آنگاه نمی توان انتظار تخمین دقیقی را داشت. بنابراین جمع آورده داده های پروژه بر اساس ویژگی های مشخص امری ضروری است و در اینجاست که می توان به نقش پررنگ پروژه های انجام شده در گذشته و مجموعه داده هایی که می توان با استفاده از آنها ایجاد نمود پی برد. در این مطالعه سعی بر این است که به بررسی نقش روش های مختلف طبقه بندی در تخمین تلاش نرم افزار بپردازیم. تفاصيل المقالة
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  7 - An Improved Algorithmic Method for Software Development Effort Estimation
  Elham Khatibi Vahid Khatibi Bardsiri
  Accurate estimating is one of the most important activities in the field of software project management. Different aspects of software projects must be estimated among which time and effort are of significant importance to efficient project planning. Due to complexity o أکثر

  Accurate estimating is one of the most important activities in the field of software project management. Different aspects of software projects must be estimated among which time and effort are of significant importance to efficient project planning. Due to complexity of software projects and lack of information at the early stages of project, reliable effort estimation is a challenging issue. In this paper, a hybrid model is proposed to estimate the effort of software projects. The proposed model is a combination of particle swarm optimization algorithm and a linear regression method in which coefficient finding is optimally performed. Moreover, the estimation equation is adjusted using project size metric so that the most accurate estimate is achieved. A relatively real large data set is employed to evaluate the performance of the proposed model and the results are compared with other models. The obtained results showed that the proposed hybrid model can improve the accuracy of estimates. تفاصيل المقالة