scholarly journals An Effective Software Effort Estimation based on Functional Points using Soft Computing Techniques

2019 ◽  
Vol 8 (10) ◽  
pp. 3729-3733
Keyword(s):  
Software Development ◽  
Soft Computing ◽  
Early Stage ◽  
Development Effort ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Software Projects ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Fuzzy Techniques

Still in this 21st century, it is a great challenge for the Project Managers to make the software projects successful. The success of software projects relies on how accurately the estimates of effort, cost and duration can be made. Most of the standard surveys stated that only 30-40% of software projects are successful and the remaining are either challenged, cancelled or failed. One of the key reasons for failure of projects is inaccurate estimations. Effort Estimation should be carried out in the early stage of Software Development Life Cycle (SDLC) and it is an essential activity to establish scope & business case of software project management activities. Over estimation or under estimation leads to failure of the software projects. Many of the stakeholders are expecting the estimation of development effort in early stage for their better bidding. There are many methodologies like KLOC, Use Case Points (UCP), Class Points, Story Points, Test Case Points, Functional Points (FP), etc. to estimate effort in the software development. To estimate the effort in the early stage of software development, UCP, Story Points and FP are more preferable. The methods for estimation may be adopted based on the project complexity, functionality, approaches etc. In order to achieve an efficient and reliable effort estimate and thereby have a proper execution of software development plan, Soft Computing Techniques can be adopted in the various organizations and different research domains. In this paper, Functional Points have been selected for effort estimation and implemented using soft computing techniques like Neural Networks and Neuro Fuzzy techniques. After examination the results are evaluated using different error measures like VAF,MMRE,RAE, RRSE and PRED. Basing on results it is observed that the Neuro Fuzzy techniques provided better effort estimates

K-Means Clustering Algorithms to Compute Software Effort Estimation

2016 ◽  
Vol 13 (10) ◽  
pp. 7093-7098 ◽  
Author(s):  
Shivakumar Nagarajan ◽  
Balaji Narayanan
Keyword(s):  
Neural Network ◽  
Software Development ◽  
Clustering Algorithms ◽  
Uncertain Data ◽  
Accurate Estimation ◽  
Development Effort ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Software Projects ◽  
Software Development Effort

Software development effort estimation is the way of predicting the effort to improve software economics. Accurate estimation of effort is the most tedious tasks in software projects. However, several methods are used to estimate the software development effort accurately. Imprecise estimation can leads to project failure due to uncertain data. In this paper, a hybrid model based on combination of Particle Swarm Optimization (PSO), K-means clustering algorithms, neural network and ABE method is proposed. The proposed method can be useful to predict better clustering and more accurate estimation and hence, there are difficulties in clustering and outliers in the software projects. The obtained results showed the better clustering result which provides the estimation result accurately. Then, neural network and Analogy methods are used which enhance the accuracy significantly.

SOFTWARE DEVELOPMENT EFFORT ESTIMATION USING CLASSICAL AND FUZZY ANALOGY: A CROSS-VALIDATION COMPARATIVE STUDY

2014 ◽  
Vol 13 (03) ◽  
pp. 1450013 ◽  
Author(s):  
FATIMA AZZAHRA AMAZAL ◽  
ALI IDRI ◽  
ALAIN ABRAN
Keyword(s):  
Software Development ◽  
Case Based Reasoning ◽  
Development Effort ◽  
Software Project ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Software Projects ◽  
Software Development Effort ◽  
Research Questions ◽  
Estimation Models

Software effort estimation is one of the most important tasks in software project management. Of several techniques suggested for estimating software development effort, the analogy-based reasoning, or Case-Based Reasoning (CBR), approaches stand out as promising techniques. In this paper, the benefits of using linguistic rather than numerical values in the analogy process for software effort estimation are investigated. The performance, in terms of accuracy and tolerance of imprecision, of two analogy-based software effort estimation models (Classical Analogy and Fuzzy Analogy, which use numerical and linguistic values respectively to describe software projects) is compared. Three research questions related to the performance of these two models are discussed and answered. This study uses the International Software Benchmarking Standards Group (ISBSG) dataset and confirms the usefulness of using linguistic instead of numerical values in analogy-based software effort estimation models.

The Application of Meta-Heuristic Algorithms to Improve the Performance of Software Development Effort Estimation Models

2015 ◽  
Vol 6 (4) ◽  
pp. 39-68 ◽  
Author(s):  
Maryam Hassani Saadi ◽  
Vahid Khatibi Bardsiri ◽  
Fahimeh Ziaaddini
Keyword(s):  
Software Development ◽  
Optimization Algorithms ◽  
Heuristic Optimization ◽  
Development Effort ◽  
Special Role ◽  
Effort Estimation ◽  
Software Projects ◽  
Software Development Effort ◽  
Estimation Models ◽  
Software Development Effort Estimation

One of the major activities in effective and efficient production of software projects is the precise estimation of software development effort. Estimation of the effort in primary steps of software development is one of the most important challenges in managing software projects. Some reasons for these challenges such as: discordant software projects, the complexity of the manufacturing process, special role of human and high level of obscure and unusual features of software projects can be noted. Predicting the necessary efforts to develop software using meta-heuristic optimization algorithms has made significant progressions in this field. These algorithms have the potent to be used in estimation of the effort of the software. The necessity to increase estimation precision urged the authors to survey the efficiency of some meta-heuristic optimization algorithms and their effects on the software projects. To do so, in this paper, they investigated the effect of combining various optimization algorithms such as genetic algorithm, particle swarm optimization algorithm and ant colony algorithm on different models such as COCOMO, estimation based on analogy, machine learning methods and standard estimation models. These models have employed various data sets to evaluate the results such as COCOMO, Desharnais, NASA, Kemerer, CF, DPS, ISBSG and Koten & Gary. The results of this survey can be used by researchers as a primary reference.

scholarly journals Investigating the Use of an Adaptive Neuro-Fuzzy Inference System in Software Development Effort Estimation

2021 ◽  
pp. 18-24
Author(s):  
Zainab Rustum Mohsin
Keyword(s):  
Software Development ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Computational Technique ◽  
Development Effort ◽  
Effort Estimation ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Software Development Effort ◽  
Software Development Effort Estimation

Modeling software development effort estimation models has been a hot research topic over the last three decades. Numerous models were proposed in these decades to predict the effort. The key challenges for future software development is providing accurate software estimation. Failure to acknowledge the accuracy of effort estimation can cause inaccurate estimation, customer disappointment, and poor software development or project failure. This research presents a novel computational technique, named adaptive neuro-fuzzy inference system (ANFIS), for the modeling of software effort estimation. It was developed utilizing the Constructive Cost Model (COCOMO) dataset. The data were randomly divided into two sets: 83% for training and 17% for testing. The mean magnitude relative-error (MMRE) and the coefficient of correlation (R) were used as assessment indices. Results showed that the accuracy of the proposed model is quite satisfactory in comparison with actual values. Moreover, a comparison study was conducted with another approach. The results showed that ANFIS produced better results in comparison with Function Point Analysis, Software Lifecycle Management, and COCOMO methods. ANFIS was found to be a potential predictive model for software development effort estimation.

What is Different about Web and Software Projects?

2008 ◽  
pp. 1-24
Author(s):  
Emilia Mendes
Keyword(s):  
Software Development ◽  
Effort Estimation ◽  
Quality Factors ◽  
Software Effort Estimation ◽  
Software Projects ◽  
Development Processes ◽  
The Web

The objective of this chapter is threefold. First is to introduce new terminology that relates specifically to hypertext, the model the Web is based upon. Second, it provides an overview of differences between Web and software development with respect to their development processes, technologies, quality factors, and measures. Third, it discusses the differences between Web effort estimation and software effort estimation.

scholarly journals Applying COCOMO II for a DO-178C Safety-Critical Software Effort Estimation

2019 ◽  
Author(s):  
Lucas Pereira dos Santos ◽  
Maurício Ferreira
Keyword(s):  
Estimation Accuracy ◽  
Development Effort ◽  
Effort Estimation ◽  
Software Effort Estimation ◽  
Software Projects ◽  
Software Application ◽  
Safety Critical ◽  
Software Development Effort ◽  
Software Practitioners ◽  
Cocomo Ii

This paper provides a real example of applying COCOMO II as an estimation technique for the required software development effort in a safety-critical software application project following the DO-178C processes. The main goal and contribution of the case study is to support the research on software effort estimation and to provide software practitioners with useful data based on a real project. We applied the method as it is, by correlating the effort multiplier factors with the complexity and objectives introduced by the DO-178C level A application, resulting in an estimated effort. The rationales for each scale factor and effort multiplier selection were also described in detail. By comparing the estimated values with the actual required data, we found a magnitude of relative error (MRE) of 40% and provided alternatives for future work in order to increase the effort estimation accuracy in safety-critical software projects.

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