Development Effort Estimation Using Soft Computing

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.

International Journal of Advanced Computer Science and Applications, 2013

Budgeting, bidding and planning of software project effort, time and cost are essential elements of any software development process. Massive size and complexity of now a day produced software systems cause a substantial risk for the development process. Inadequate and inefficient information about the size and complexity results in an ambiguous estimates that cause many losses. Project managers cannot adequately provide good estimate for both the effort and time needed. Thus, no clear release day to the market can be defined. This paper presents two new models for software effort estimation using fuzzy logic. One model is developed based on the famous COnstructive COst Model (COCOMO) and utilizes the Source Line Of Code (SLOC) as input variable to estimate the Effort (E); while the second model utilize the Inputs, Outputs, Files, and User Inquiries to estimate the Function Point (FP). The proposed fuzzy models show better estimation capabilities compared to other reported models in the literature and better assist the project manager in computing the software required development effort. The validation results are carried out using Albrecht data set.

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. Software cost estimation is a challenging and onerous task. Estimation by analogy is one of the convenient techniques in software effort estimation field. However, the methodology used for the estimation of software effort by analogy is not able to handle the categorical data in an explicit and accurate manner. Different techniques have, so far, been used like regression analysis, mathematical derivations, simulation, neural network, genetic algorithm, soft computing, fuzzy logic modelling etc. This paper aims to utilize soft computing techniques to improve the accuracy of software effort estimation. In this approach fuzzy logic is used with particle swarm optimization to estimate software development effort. The model has been calibrated on 30 projects, taken from NASA dataset. The results of this model are compared with COCOMO II and Alaa Sheta Model. The proposed model yields better results in terms of MMRE.

Computing Research Repository, 2010

Software effort estimation at early stages of project development holds great significance for the industry to meet the competitive demands of today's world. Accuracy, reliability and precision in the estimates of effort are quite desirable. The inherent imprecision present in the inputs of the algorithmic models like Constructive Cost Model (COCOMO) yields imprecision in the output, resulting in erroneous effort estimation. Fuzzy logic based cost estimation models are inherently suitable to address the vagueness and imprecision in the inputs, to make reliable and accurate estimates of effort. In this paper, we present an optimized fuzzy logic based framework for software development effort prediction. The said framework tolerates imprecision, incorporates experts knowledge, explains prediction rationale through rules, offers transparency in the prediction system, and could adapt to changing environments with the availability of new data. The traditional cost estimation model COCOMO is extended in the proposed study by incorporating the concept of fuzziness into the measurements of size, mode of development for projects and the cost drivers contributing to the overall development effort.

Evolutionary Computation, 2008. …, 2008

Accurate estimation of the software effort and schedule affects the budget computation. Bidding for contracts depends mainly on the estimated cost. Inaccurate estimates will lead to failure of making a profit, increased probability of project incompletion and delay of the project delivery date. In this paper, we explore the use of Soft Computing Techniques to build a suitable model structure to utilize improved estimations of software effort for NASA software projects. In doing so, we plan to use Particle Swarm Optimization (PSO) to tune the parameters of the famous COnstructive COst MOdel (COCOMO). We plan also to explore the advantages of Fuzzy Logic to build a set of linear models over the domain of possible software Line Of Code (LOC). The performance of the developed model was evaluated using NASA software projects data set [1]. A comparison between COCOMO tuned-PSO, Fuzzy Logic (FL), Halstead, Walston-Felix, Bailey-Basili and Doty models were provided.

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.

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.

2007

The development of software has always been characterized by parameters that possess certain level of fuzziness. This requires that some degree of uncertainty be introduced in the models, in order to make the models realistic. Fuzzy logic fares well in this area. Many of the problems of the existing effort estimation models can be solved by incorporating fuzzy logic. Besides, fuzzy logic had been combined with algorithmic, non-algorithmic effort estimation models as well as a combination of them to deal with the inherent uncertainty issues. This paper also described an enhanced fuzzy logic model for the estimation of software development effort. The model (FLECE) possesses similar capabilities as the previous fuzzy logic model. In addition to that, the enhancements done in FLECE improved the empirical accuracy of the previous model in terms of MMRE (Mean Magnitude of Relative Error) and threshold-oriented prediction measure or prediction quality (pred).

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.

2016

From the very beginning COCOMO model was used for estimation process, at that era fuzzy logic or perhaps artificial intelligence wasn't grounded into solid. In present scenario projects are very large and are generally globally distributed as well as in software improvement, software effort estimation is one of the crucial steps particularly for offshore projects. The principle aim of this paper is always to emphasize all the uncertainties that had been faced in earlier time, but by means of fuzzy logic precision of estimation was improved. Fuzzy-logic technique primarily based software effort estimation models will be more reliable and agreeable, especially for significant and complex initiatives KeywordsEffort Estimation, Fuzzy logic, Constructive Cost model (COCOMO), Fuzzification, Dfuzzyfication.