Software Cost Estimation Using Soft Computing Techniques 2012.pdf

International Journal of Machine Learning and Computing, 2011

Software effort estimation plays a critical role in project management. Erroneous results may lead to overestimating or underestimating effort, which can have catastrophic consequences on project resources. Machine-learning techniques are increasingly popular in the field. Fuzzy logic models, in particular, are widely used to deal with imprecise and inaccurate data. e main goal of this research was to design and compare three different fuzzy logic models for predicting software estimation effort: Mamdani, Sugeno with constant output, and Sugeno with linear output. To assist in the design of the fuzzy logic models, we conducted regression analysis, an approach we call "regression fuzzy logic." State-of-the-art and unbiased performance evaluation criteria such as standardized accuracy, effect size, and mean balanced relative error were used to evaluate the models, as well as statistical tests. Models were trained and tested using industrial projects from the International Software Benchmarking Standards Group (ISBSG) dataset. Results showed that data heteroscedasticity affected model performance. Fuzzy logic models were found to be very sensitive to outliers. We concluded that when regression analysis was used to design the model, the Sugeno fuzzy inference system with linear output outperformed the other models.

2011

Software development effort estimation is among one of the most challenging jobs that software developers need to perform. Due to the lack of information during the early stages of software development, the developers often express their inability to estimate accurately the effort, cost and schedule of the software under consideration. This inaccuracy in estimation leads to monetary losses as well delay in delivery of the product. In this paper, a soft computing based technique is explored to overcome the problems of uncertainty and imprecision resulting in improved process of software development effort estimation. In doing so, fuzzy logic is applied to different parameters of Constructive Cost Model (COCOMO) II. Results shows that the value of MMRE (Mean of Magnitude of Relative Error) and pred obtained by means of applying fuzzy logic is much better than of MMRE of algorithmic model. The validation of results is carried out on COCOMO dataset. KeywordsSoftware Cost Estimation, COC...

Algorithms, Methods, and Techniques

Software development has become an essential investment for many organizations. Software engineering practitioners have become more and more concerned about accurately predicting the cost of software products to be developed. Accurate estimates are desired but no model has proved to be successful at effectively and consistently predicting software development cost. This chapter investigates the use of the soft computing approaches in predicting the software development effort. Various statistical and intelligent techniques are employed to estimate software development effort. Further, based on the abovementioned techniques, ensemble models are developed to forecast software development effort. Two types of ensemble models viz., linear (average) and nonlinear are designed and tested on COCOMO’81 dataset. Based on the experiments performed on the COCOMO’81 data, it was observed that the nonlinear ensemble using radial basis function network as arbitrator outperformed all the other ens...

Ijca Proceedings on National Conference on Emerging Trends in Information Technology, 2014

Accurately estimating the code size, cost, effort and schedule is probably the leading vital challenge facing code developers lately. It's major implications for the management of code development as a consequences of every the overestimates and underestimates have direct impact for inflicting damage to code companies. Heap of models square measure projected over the years by varied researchers for ending effort estimations. in addition variety of the studies for early stage effort estimations promoter the importance of early estimations. New paradigms offeralternatives to estimate the code development effort, specially the machine Intelligence (CI) that exploits mechanisms of interaction between humans and processes domain information with the intention of building intelligent systems (IS). Among IS, Artificial Neural Network and logic unit of quantity the two most popular soft computing techniques for code development effort estimation. The aim of this study is to research soft computing techniques inside the there models and to bring thorough review of code and project estimation techniques existing in trade.

Soft Computing-A Fusion of …, 2006

Accurate software estimation such as cost estimation, quality estimation and risk analysis is a major issue in software project management. In this paper, we present a soft computing framework to tackle this challenging problem. We first use a preprocessing neurofuzzy inference system to handle the dependencies among contributing factors and decouple the effects of the contributing factors into individuals. Then we use a neuro-fuzzy bank to calibrate the parameters of contributing factors. In order to extend our framework into fields that lack of an appropriate algorithmic model of their own, we propose a default algorithmic model that can be replaced when a better model is available. One feature of this framework is that the architecture is inherently independent of the choice of algorithmic models or the nature of the estimation problems. By integrating neural networks, fuzzy logic and algorithmic models into one scheme, this framework has learning ability, integration capability of both expert knowledge and project data, good interpretability, and robustness to imprecise and uncertain inputs. Validation using industry project data shows that the framework produces good results when used to predict software cost.

The software cost estimation is the process of predicting the most realistic amount of effort required to develop or maintain software based on incomplete, uncertain and noisy input. Effort estimates may be used as input to project plans, iteration plans, budgets, and investment analyses, pricing processes and bidding rounds. For example, estimation of value estimation and danger examination is a real issue in programming undertaking administration. As programming advancement has turned into a fundamental speculation for some associations, precise programming expense estimation models are expected to adequately foresee, screen, control and evaluate programming improvement. Programming expense estimation is a testing and cumbersome errand. Then again, the approach utilized for the estimation of programming exertion by relationship is not ready to handle the unmitigated information in an unequivocal and exact way. The nature of an expense estimation model is less ascribed to the introductory assessment, but instead the pace at which the appraisals merges to the genuine expense of the task. COCOMO is a well known algorithmic model for expense estimation whose expense variables can be customized to the individual improvement environment, which is imperative for the precision of the expense gauges. More than one strategy for expense estimation ought to be carried out so that there is some correlation accessible for the evaluations. This is particularly imperative for extraordinary undertakings. Cost estimation must be done more diligently throughout the project life cycle so that in the future there are fewer surprises and unforeseen delays in the release of a product. In this paper, we present a soft computing framework to tackle this challenging problem. Estimating the work-effort and the schedule required to develop and/or maintain a software system is one of the most critical activities in managing software projects.

Journal of Computer Science, 2010

Problem statement: Software development effort estimation is the process of predicting the most realistic use of effort required for developing software based on some parameters. It has always characterized one of the biggest challenges in Computer Science for the last decades. Because time and cost estimate at the early stages of the software development are the most difficult to obtain and they are often the least accurate. Traditional algorithmic techniques such as regression models, Software Life Cycle Management (SLIM), COCOMO II model and function points, require an estimation process in a long term. But, nowadays that is not acceptable for software developers and companies. Newer soft computing techniques to effort estimation based on non-algorithmic techniques such as Fuzzy Logic (FL) may offer an alternative for solving the problem. This work aims to propose a new fuzzy logic realistic model to achieve more accuracy in software effort estimation. The main objective of this research was to investigate the role of fuzzy logic technique in improving the effort estimation accuracy by characterizing inputs parameters using two-side Gaussian function which gave superior transition from one interval to another. Approach: The methodology adopted in this study was use of fuzzy logic approach rather than classical intervals in the COCOMO II. Using advantages of fuzzy logic such as fuzzy sets, inputs parameters can be specified by distribution of its possible values and these fuzzy sets were represented by membership functions. In this study to get a smoother transition in the membership function for input parameters, its associated linguistic values were represented by twoside Gaussian Membership Functions (2-D GMF) and rules. Results: After analyzing the results attained by means of applying COCOMO II and proposed model based on fuzzy logic to the NASA dataset and created an artificial dataset, it had been found that proposed model was performing better than ordinal COCOMO II and the achieved results were closer to the actual effort. The relative error for proposed model using two-side Gaussian membership functions is lower than that of the error obtained using ordinal COCOMO II. Conclusion: Based on the achieved results, it was concluded that, using soft computation approaches such as fuzzy logic and their advantages, good predication; adaption; understandability and the accuracy of software effort estimation can be improved and the estimation can be very close to the actual effort. This novelty model will lead researchers to focus on benefits of non-algorithmic models to overcome the estimation problems.

Applied Computer Systems

During the recent years, numerous endeavours have been made in the area of software development effort estimation for calculating the software costs in the preliminary development stages. These studies have resulted in the offering of a great many of the models. Despite the large deal of efforts, the substantial problems of the offered methods are their dependency on the used data collection and, sometimes, their lack of appropriate efficiency. The current article attempts to present a model for software development effort estimation through making use of evolutionary algorithms and neural networks. The distinctive characteristic of this model is its lack of dependency on the collection of data used as well as its high efficiency. To evaluate the proposed model, six different data collections have been used in the area of software effort estimation. The reason for the application of several data collections is related to the investigation of the model performance independence of the...

One of the most important tasks of a software development life cycle is to estimate software time and effort. Accuracy in effort estimation is still a great challenge in the software industry. Conventional models like the Constructive Cost Model (COCOMO) are quite helpful but they are unable to manage the unsure nature of the software projects particularly within the early part of the development criterion. There has been considerable work exhausted in this area using neural networks and conjointly some work done using fuzzy logic. These models offer additional exactitude however they " re heavily obsessed on the scale of the training set. The aim of this paper is to develop and evaluate a neuro-fuzzy model to estimate software development effort. Our proposed work is compared with neural network approach and fuzzy logic approach based on the value of MMRE (Mean of Magnitude of Relative Error) .The projected Neuro-fuzzy model was found to have lower MMRE value.

Software cost estimation is a challenging and onerous task. Estimation by analogy is one of the expedient techniques in software effort estimation field. However, the methodology utilized for the estimation of software effort by analogy is not able to handle the categorical data in an explicit and precise manner. Early software estimation models are based on regression analysis or mathematical derivations. Today's models are based on simulation, neural network, genetic algorithm, soft computing, fuzzy logic modelling etc. This paper aims to utilize a fuzzy logic model to improve the accuracy of software effort estimation. In this approach fuzzy logic is used to fuzzify input parameters of COCOMO II model and the result is defuzzified to get the resultant Effort. Triangular fuzzy numbers are used to represent the linguistic terms in COCOMO II model. The results of this model are compared with COCOMO II and Alaa Sheta Model. The proposed model yields better results in terms of MMRE, PRED(n) and VAF.