Efficient Indicators to Evaluate the Status of Software Development Effort Estimation inside the Organizations

Development effort is an undeniable part of the project management which considerably influences the success of project. Inaccurate and unreliable estimation of effort can easily lead to the failure of project. Due to the special specifications, accurate estimation of effort in the software projects is a vital management activity that must be carefully done to avoid from the unforeseen results. However numerous effort estimation methods have been proposed in this field, the accuracy of estimates is not satisfying and the attempts continue to improve the performance of estimation methods. Prior researches conducted in this area have focused on numerical and quantitative approaches and there are a few research works that investigate the root problems and issues behind the inaccurate effort estimation of software development effort. In this paper, a framework is proposed to evaluate and investigate the situation of an organization in terms of effort estimation. The proposed framework includes various indicators which cover the critical issues in field of software development effort estimation. Since the capabilities and shortages of organizations for effort estimation are not the same, the proposed indicators can lead to have a systematic approach in which the strengths and weaknesses of organizations in field of effort estimation are discovered.Comment: 10 page

An investigation of effective factors on effort estimation of software projects inside the organization

Management of software projects has become a challenging issue during the last decade. The latest published statistics related to the status of software projects shows a substantial rate of fail, which has raised a critical challenge for project managers. In prior studies, inaccurate effort estimation has been mentioned as the most important reason behind the fail of software projects. The latest published papers in this area reported that the accuracy of existing estimation models are not convincing and the flexibility of models is not enough to be utilized for different types of project. Considering an estimation process (estimation model, materials, techniques and so on) to be used in a wide range of organizations regardless of the capabilities and specifications of organization is the main problem leads the current estimation models towards inaccurate and unreliable estimates. As a solution, each organization must know its strengths, weaknesses, opportunities, threats, capabilities and all aspects related to effort estimation. In other words, the real status of organization in terms of effort estimation must be clarified so that the reasonable decisions can be made to reach an efficient process of effort estimation. This research conducts a survey in which the important aspects of effort estimation including estimation process, limitations, management issues and project attributes are evaluated. Unlike prior survey-based studies conducted in the past, this research focuses on importance of project attributes and management issues. In addition, this research tries to integrate the concepts considered by prior studies separately. Moreover the relationships between the key concepts related to effort estimation are evaluated and discussed. Finally, a form is designed in which the results are efficiently summarized, which clearly depicts the real status of organization if field of effort estimation. This survey is conducted on a sample of 135 developers working in a large software company

A hybrid method for increasing the accuracy of software development effort estimation

Since software development environments, methods and tools are changing rapidly, the importance of accurate estimations in software projects is increasing significantly. Inaccurate estimations can lead to unpleasant results in the software projects so that many projects are failed at the early stages of the project. During the recent years, numerous estimation methods have been proposed that most of which are based on statistical techniques. Among all existing methods, simplicity of analogy based method makes it so common in this field. Analogy methods usually present accurate estimations but if the level of non normality in the software project datasets is high or type of most project features is categorical, these methods are confronted with inaccurate estimation problem. In this paper, genetic algorithm has been used under a new framework to improve the performance of analogy methods. A large dataset has been employed to evaluate the performance of the proposed method and the results have been compared with the other estimation methods. The results showed that the proposed method outperformed the other methods considerabl

Towards improvement of analogy-based software development effort estimation: a review

In this paper a systematic review is conducted to investigate the structure, components, techniques, evaluation procedure, and comparison scope related to prior ABE-based studies. The undeniable role of accurate development effort estimation in the success of software project management has attracted the attention of researchers over the past few years. Among various algorithmic and non-Algorithmic estimation methods, analogy based estimation (ABE) is a widely accepted method due to its simplicity and estimation capability. This paper investigates the improvement process of ABE method during 2000 to 2012. Six research questions are defined to be addressed through evaluation of prior ABE-based studies. The review domain includes 24 papers selected through a tough filtration process. The results show that improvement of ABE can be performed through adjustment, grey theory, attribute weighting and attribute selection techniques. Moreover, ABE configurations can significantly affect the results

A New Fuzzy Clustering Based Method to Increase the Accuracy of Software Development Effort Estimation

Abstract: Project planning plays a significant role in software projects so that imprecise estimations often lead to the project faults or dramatic outcomes for the project team. In recent years, various methods have been proposed to estimate the software development effort accurately. Among all proposed methods the non algorithmic methods by using soft computing techniques have presented considerable results. Complexity and uncertain behavior of software projects are the main reasons for going toward the soft computing techniques. In this paper a hybrid system based on combining C-Means clustering, neural network and analogy method is proposed. Since, there are complicated and non linear relations among software project features, the proposed method can be useful to interpret such relations and to present more accurate estimations. The obtained results showed that fuzzy clustering could decrease the negative effect of irrelevant projects on accuracy of estimations. In addition, evaluation of proposed hybrid method showed the significant improvement of accuracy as compared to the neural network the analogy method and statistical methods

A PSO-based model to increase the accuracy of software development effort estimation

Development effort is one of the most important metrics that must be estimated in order to design the plan of a project. The uncertainty and complexity of software projects make the process of effort estimation dif?cult and ambiguous. Analogy-based estimation (ABE) is the most common method in this area because it is quite straightforward and practical, relying on comparison between new projects and completed projects to estimate the development effort. Despite many advantages, ABE is unable to produce accurate estimates when the importance level of project features is not the same or the relationship among features is dif?cult to determine. In such situations, ef?cient feature weighting can be a solution to improve the performance of ABE. This paper proposes a hybrid estimation model based on a combination of a particle swarm optimization (PSO) algorithm and ABE to increase the accuracy of software development effort estimation. This combination leads to accurate identi?cation of projects that are similar, based on optimizing the performance of the similarity function in ABE. A framework is presented in which the appropriate weights are allocated to project features so that the most accurate estimates are achieved. The suggested model is ?exible enough to be used in different datasets including categorical and non-categorical project features. Three real data sets are employed to evaluate the proposed model, and the results are compared with other estimation models. The promising results show that a combination of PSO and ABE could signi?cantly improve the performance of existing estimation models

Application of Advanced Nanomaterials for Kidney Failure Treatment and Regeneration

The implementation of nanomedicine not only provides enhanced drug solubility and reduced off-target adverse effects, but also offers novel theranostic approaches in clinical practice. The increasing number of studies on the application of nanomaterials in kidney therapies has provided hope in a more efficient strategy for the treatment of renal diseases. The combination of biotechnology, material science and nanotechnology has rapidly gained momentum in the realm of therapeutic medicine. The establishment of the bedrock of this emerging field has been initiated and an exponential progress is observed which might significantly improve the quality of human life. In this context, several approaches based on nanomaterials have been applied in the treatment and regeneration of renal tissue. The presented review article in detail describes novel strategies for renal failure treatment with the use of various nanomaterials (including carbon nanotubes, nanofibrous membranes), mesenchymal stem cells-derived nanovesicles, and nanomaterial-based adsorbents and membranes that are used in wearable blood purification systems and synthetic kidneys