A CASE Study on Software Project Development Cost, Schedule & Effort Estimation

This paper theme is to provide a case study of Software Project Development cost, effort, and schedule estimation. From recent past, a remarkable research takes place in developing different techniques on software effort and cost estimation. Making estimation before start of any project is necessary to be able to plan and manage any project. The estimate is an intelligent guess for the project resources. Nowadays, software has become a major contributor to economic growth for any nation. Making an estimate before starting any software project is vital for the project managers and key stakeholders. Major project milestones such as project schedules, budgeting, resource allocation, and project delivery dates are set on theeffort and cost estimates. Thus, the reliability of the estimation leads any project success or otherwise fail. In this article, author's idea is to work with function point analysis and include the concept of workforce scheduling in a better way while taking the decision in the contract phase. That leads to strengthening the relations between the developer and the customer. Basically, size is a main measured unit of the software project. Based on the size and other functionalities, the software managers estimate the total effort required to develop the project. From the effort and work schedule, the total cost can be estimated.Â

REBEE- Reusability Based Effort Estimation Technique using Dynamic Neural Network

Software Effort Estimation has been researched for over 25 years but until today no real effective model could be designed that could efficiently gauge the effort required for heterogeneous project data. Reusability factors of software development have been used to design a new effort estimation model called REBEE. This encompasses the usage of Fuzzy Logic and Dynamic Neural Networks. The experimental evaluation of the model depicts efficient effort estimation over varied project types

Open Hybrid Model: A New Ensemble Model for Software Development Cost Estimation

Given various features of a software project, it may face different administrative challenges requiring right decisions by software project managers. A major challenge is to estimate software development cost for which different methods have been proposed by many researchers. According to the literature, the capability of a proposed model or method is demonstrated in a specific set of software projects. Hence, the aim of this study is to present a model to take advantage of the capabilities of various software development cost estimation models and methods simultaneously. For this purpose, a new model called "open hybrid model" was proposed based on the firefly algorithm. The proposed model includes an extensible bank of estimation methods. The model also includes an extensible bank of rules to describe the relation between existing methods. Considering project conditions, the proposed model tries to find the best rule for combining estimation methods in the methods bank. Three datasets of real projects were used to evaluate the precision of the proposed model, and the results were compared with those of other 11 methods. The results were compared based on performance parmeters widely used to show the accuracy and stability of estimation models. According to the results, the open hybrid model was able to select the most appropriate methods present in the methods bank

Software Effort Estimation Accuracy Prediction of Machine Learning Techniques: A Systematic Performance Evaluation

Software effort estimation accuracy is a key factor in effective planning, controlling and to deliver a successful software project within budget and schedule. The overestimation and underestimation both are the key challenges for future software development, henceforth there is a continuous need for accuracy in software effort estimation (SEE). The researchers and practitioners are striving to identify which machine learning estimation technique gives more accurate results based on evaluation measures, datasets and the other relevant attributes. The authors of related research are generally not aware of previously published results of machine learning effort estimation techniques. The main aim of this study is to assist the researchers to know which machine learning technique yields the promising effort estimation accuracy prediction in the software development. In this paper, the performance of the machine learning ensemble technique is investigated with the solo technique based on two most commonly used accuracy evaluation metrics. We used the systematic literature review methodology proposed by Kitchenham and Charters. This includes searching for the most relevant papers, applying quality assessment criteria, extracting data and drawing results. We have evaluated a state-of-the-art accuracy performance of 28 selected studies (14 ensemble, 14 solo) using Mean Magnitude of Relative Error (MMRE) and PRED (25) as a set of reliable accuracy metrics for performance evaluation of accuracy among two techniques to report the research questions stated in this study. We found that machine learning techniques are the most frequently implemented in the construction of ensemble effort estimation (EEE) techniques. The results of this study revealed that the EEE techniques usually yield a promising estimation accuracy than the solo techniques.Comment: Pages: 27 Figures: 15 Tables: