Research Paper on Software Cost Estimation Using Fuzzy Logic

Software cost estimation is one of the biggest challenges in these days due to tremendous completion. You have to bid so close so that you can get the consignment if your cost estimation is too low are too high in that cases organization has to suffer that why it becomes very crucial to get consignment. One of the important issues in software project management is accurate and reliable estimation of software time, cost, and manpower, especially in the early phase of software development. Software attributes usually have properties of uncertainty and vagueness when they are measured by human judgment. A software cost estimation model incorporates fuzzy logic can overcome the uncertainty and vagueness of software attributes. However, determination of the suitable fuzzy rule sets for fuzzy inference system plays an important role in coming up with accurate and reliable software estimates. The objective of our research was to examine the application of applying fuzzy logic in software cost estimation that can perform more accurate result. In fuzzy logic there are various membership function for example Gaussian, triangular, trapezoidal and many more. Out of these by hit and trial method we find triangular membership function (MF) yields least MRE and MMRE and this MRE must be less than 25%. In our research this value came around 15% which is very fair enough to estimate. Cost can be found out using the equation if payment is known Cost = Effort * (Payment Month). Therefore the effort needed for a particular software project using fuzzy logic is estimated. In our research NASA (93) data set used to calculate fuzzy logic COCOMO II. From this table size of code and actual effort has been taken. In end after comparing the result we found that our proposed technique is far superior to base work

OPTIMIZATION TECHNIQUE FOR SOFTWARE COST ESTIMATION USING NEURAL NETWORK

Last few decade software accomplishment admiration models developed, authentic estimates of the software activity beneath development is still unachievable goal. Recently advisers are alive on the development of new models and the advance of the absolute ones application bogus intelligence techniques. Designing of ANN (Artificial Neural Network) to archetypal a circuitous set of accord amid the abased capricious (effort) and the absolute variables (cost drivers) makes an apparatus for estimation. This cardboard presents an achievement assay of Multi ANNs in accomplishment estimation. We accept apish Back propagation ANN created by MATLAB Neural Network Apparatus application NASA dataset

Fuzzy Use Case Points as a Basis for Effort Estimation

Many software development projects encounter problems related to over- or under-estimation of effort. Accurate effort estimation is crucial for successful project management, but it can be challenging when resources are limited, and little is known about the project. The commonly used method for effort estimation is Use Case Points (UCP), which is mainly used for application-based objects and takes use cases as input. However, UCP has weaknesses, particularly in the high variation of weight factor values for Unadjusted Use Case Weight (UUCW). To address this problem, Fuzzy Use Case Points (FUCP), which is a combination of fuzzy logic and use case points, can be used. By applying fuzzy logic to the UUCW category, FUCP derives new weight factor values for UUCW. The implementation of FUCP to calculate effort estimation in ten government-based projects in this research has shown that FUCP yields the closest value to the actual effort required. It has also been demonstrated that FUCP outperforms UCP in terms of accuracy, with an improvement of 6.51%

Optimizing Effort Parameter of COCOMO II Using Particle Swarm Optimization Method

Estimating the effort and cost of software is an important activity for software project managers. A poor estimate (overestimates or underestimates) will result in poor software project management. To handle this problem, many researchers have proposed various models for estimating software cost. Constructive Cost Model II (COCOMO II) is one of the best known and widely used models for estimating software costs. To estimate the cost of a software project, the COCOMO II model uses software size, cost drivers, scale factors as inputs. However, this model is still lacking in terms of accuracy. To improve the accuracy of COCOMO II model, this study examines the effect of the cost factor and scale factor in improving the accuracy of effort estimation. In this study, we initialized using Particle Swarm Optimization (PSO) to optimize the parameters in a model of COCOMO II. The method proposed is implemented using the Turkish Software Industry dataset which has 12 data items. The method can handle improper and uncertain inputs efficiently, as well as improves the reliability of software effort. The experiment results by MMRE were 34.1939%, indicating better high accuracy and significantly minimizing error 698.9461% and 104.876%

Improving Software Cost Estimation With Function Points Analysis Using Fuzzy Logic Method

Function Points Analysis (FPA) is amongst the most generally used method to assess software cost estimation frameworks. This process speaks to the measurement of an undertaking, application, and function by its relative functional complexity. In general, it has numerous effective applications used in both industry and scholarly research. This is noticed that customized estimate technologies which can confront genuine challenges utilizing on programming building information is normally constrained, loosely gathered and deficient. To enquire these queries composite programming models, blend of information, fuzzy logic and master judgment is proposed. This is trusted that outcomes announced here will animate, renew investigation of fuzzy logic to genuine programming designing issues. In this research paper, we use Function Points and apply some new models to pick up a superior estimation of programming properties. The utilization of ideas and characteristics from the fuzzy set hypothesis to stretch out function points analysis to fuzzy function points analysis. Fuzzy hypothesis tries to construct formal quantitative arrangement equipped for imitating imprecision of the human information. With the function points created by Fuzzy FPA, an estimate value for example, expenses/cost and software development can be more correctly determined

INTEGRATION OF FUZZY LOGIC METHOD AND COCOMO II ALGORITHM TO IMPROVE PREDICTION TIMELINESS AND SOFTWARE DEVELOPMENT COST

This study discusses improving the prediction of timeliness and cost of software development using the Constructive Cost Model II (COCOMO II) method and the application of Fuzzy Logic. And aims to obtain accurate time and cost prediction estimates on software development projects to obtain maximum cost results for a software development project. This study utilizes an adaptive fuzzy logic model to improve the timeliness of software development and cost estimates. Using the advantages of fuzzy set logic and producing accurate software attributes to increase the prediction of the time and price of software development. The fuzzy model uses the Two-D Gaussian Membership Function (2-D GMF) to make the software attributes more detailed in terms of the range of values. In COCOMO I, NASA98 data set; and four data projects from software companies in Indonesia were used to evaluate the proposed Fuzzy Logic COCOMO II, commonly known as FL-COCOMO II. Using the Mean of Magnitude of Relative Error (MMRE) and the Pred evaluation technique, the results showed that FL-COCOMO II produced less MMRE than COCOMO I, and the Pred value (25%) in Fuzzy-COCOMO II was higher than COCOMO I. In addition, FL-COCOMO II showed an 8.03% increase in prediction accuracy using MMRE compared to the original COCOMO. Using the advantages of Fuzzy Logic, such as accurate predictions, adaptation, and understanding can improve the accuracy of the timeliness and cost estimates of the software

Enhancing Use Case Points Estimation Method Using Soft Computing Techniques

Software estimation is a crucial task in software engineering. Software estimation encompasses cost, effort, schedule, and size. The importance of software estimation becomes critical in the early stages of the software life cycle when the details of software have not been revealed yet. Several commercial and non-commercial tools exist to estimate software in the early stages. Most software effort estimation methods require software size as one of the important metric inputs and consequently, software size estimation in the early stages becomes essential. One of the approaches that has been used for about two decades in the early size and effort estimation is called use case points. Use case points method relies on the use case diagram to estimate the size and effort of software projects. Although the use case points method has been widely used, it has some limitations that might adversely affect the accuracy of estimation. This paper presents some techniques using fuzzy logic and neural networks to improve the accuracy of the use case points method. Results showed that an improvement up to 22% can be obtained using the proposed approach

Software Development Effort Estimation Using Regression Fuzzy Models

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. The 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.Comment: This paper has been accepted in January 2019 in Computational Intelligence and Neuroscience Journal (In Press