Software Cost Estimation Methods Research Articles

Software cost estimation tool: A App based application, estimate the cost of software project

This paper presents the design and implementation of a software cost estimation tool integrated into a mobile application developed using Flutter. The tool incorporates various techniques for software cost estimation, including expert judgment, function point analysis, 3D point analysis, and the COCOMO model. The purpose of the program is to give software engineers and project managers a practical and effective tool for calculating the time and money needed for software development projects. The paper provides a thorough explanation of each estimation technique’s implementation, along with a discussion of the app’s main features and functionalities. Because of the app’s intuitive and user-friendly design, users can quickly enter project data and get precise cost estimates. The tool’s efficacy is assessed using case studies and contrasts with other software cost estimation methods currently in use. The outcomes show that the app can produce trustworthy and precise cost estimates, which makes it an important resource for software development projects.

A Hybrid Artificial Bee Colony and Artificial Fish Swarm Algorithms for Software Cost Estimation

Software cost estimation (SCE), estimating the cost and time required for software development, plays a highly significant role in managing software projects. A somewhat accurate SCE is necessary for a software project to be successful. It allows effective control of construction time and cost. In the past few decades, various models have been presented to evaluate software projects, including mathematical models and machine learning algorithms. In this paper, a new model based on the hybrid of the artificial fish swarm algorithm (AFSA) and the artificial bee colony (ABC) algorithm is presented for SCE. The initial population of AFSA, which includes the values of the effort factors, is generated using the ABC algorithm. ABC algorithm is used to solve the problems of the AFSA algorithm such as population diversity and getting stuck in a local optimum. ABC algorithm achieves the best solutions using observer and scout bees. The evaluation of the combined method has been implemented on eight different data sets and evaluated based on eight different criteria such as mean magnitude of relative error and PRED (0.25). The proposed method is more error-free than current SCE methods, according to the results. The error value of the proposed method is lower on NASA60, NASA63, and NASA93 datasets.

Enhancing Delphi Method with Algorithmic Estimates for Software Effort Estimation: An Experimental Study

Literature review shows that more accurate software effort and cost estimation methods are needed for software project management success. Expert judgment and algorithmic model estimation are two predominant methods discussed in the literature. Both are reported almost at the comparable level of accuracy performance. The combination of the two methods is suggested to increase the estimation accuracy. Delphi method is an encouraging structured expert judgment method for software effort group estimation but surprisingly little was reported in the literature. The objective of this study is to test if the Delphi estimates will be more accurate if the participants in the Delphi process are exposed to the algorithmic estimates. A Delphi experiment where the participants in the Delphi process were exposed to three algorithmic estimates – Function Points, COCOMO estimates, and Use Case Points, was therefore conducted. The findings show that the Delphi estimates are slightly more accurate than the statistical combination of individual expert estimates, but they are not statistically significant. However, the Delphi estimates are statistically significant more accurate than the individual estimates. The results also show that the Delphi estimates are slightly less optimistic than the statistical combination of individual expert estimates but they are not statistically significant either. The adapted Delphi experiment shows a promising technique for improving the software cost estimation accuracy.

Enhancing Delphi Method with Algorithmic Estimates for Software Effort Estimation: An Experimental Study

Literature review shows that more accurate software effort and cost estimation methods are needed for software project management success. Expert judgment and algorithmic model estimation are two predominant methods discussed in the literature. Both are reported almost at the comparable level of accuracy performance. The combination of the two methods is suggested to increase the estimation accuracy. Delphi method is an encouraging structured expert judgment method for software effort group estimation but surprisingly little was reported in the literature. The objective of this study is to test if the Delphi estimates will be more accurate if the participants in the Delphi process are exposed to the algorithmic estimates. A Delphi experiment where the participants in the Delphi process were exposed to three algorithmic estimates –Function Points, COCOMO estimates, and Use Case Points, was therefore conducted. The findings show that the Delphi estimates are slightly more accurate than the statistical combination of individual expert estimates, but they are not statistically significant. However, the Delphi estimates are statistically significant more accurate than the individual estimates. The results also show that the Delphi estimates are slightly less optimistic than the statistical combination of individual expert estimates but they are not statistically significant either. The adapted Delphi experiment shows a promising technique for improving the software cost estimation accuracy.

The trend of software cost estimation in Ethiopia Case study : Addis Ababa

For a different item, you can purchase it. There are different costs to purchase that you can analysis the benefit and loses, even if on the software it has different approaches to analysis their wages, the developer company can get their analysis by the different methods. There are a lot of software cost estimation methods are appearing in different years but still, those methods have their own drawback on making a correct effort and scheduling estimation, here in the paper Today there are different software cost estimation methods that the software company uses from the requirement to implementation phase. Corrected cost estimation supports us to complete the project on planned times and budgets. This paper is mainly presenting the current situations of cost estimation on software placed in Addis Abebas different software compony
 

Investigating the Relationship Between SLOC and Logical Database Measures to Improve the Early Estimation of Software Cost

Project planning is a critical activity in the software development life cycle. At the early stages of a project, the managers need to estimate required time, effort and cost to plan, track and then to deliver the project successfully. Many studies have attempted to provide methods for precise software cost estimation. The current software cost estimation methods are mainly based on software size estimation and functional system requirements. The main assumption of this study is that, as the primary source of complexity in today’s software is the interaction between the database and the user, database measures may provide inputs allowing current software estimation methods to achieve more accurate results. Accordingly, this study attempts to gain insights from objective measures, collected through the logical database model of software systems, for better prediction of the software’s effort and hence cost through software lines of code (SLOC) measure. For this purpose, more than 2.5 million lines of code developed by four different companies, for 79 different software packages with their related database design measures, are analyzed. The results of this study show that there is a close correlation between the software size and database design measure, namely, the number of tables which can be collected at the logical database design stage. By adapting this result, the current estimation models could be improved significantly.

Design & Analysis of Cost Estimation for New Mobile-COCOMO Tool for Mobile Application

Software cost estimation is a resource forecasting method, which is required by the software development process. However, estimating the workload, schedule and cost of a software project is a complex task because it involves predicting the future using historical project data and extrapolating to see future values. For cost estimates for software projects, several methods are used. Among the various software cost estimation methods available, the most commonly used technology is the COCOMO method. Similarly, to calculate software costs, there are several cost estimating tools available for software developers to use. But these released cost estimation tools can only provide parameters (i.e. cost, development time, average personnel) for large software with multiple lines of code. However, if a software developer wants to estimate the cost of a small project that is usually a mobile application, the available tools will not give the right results. Therefore, to calculate the cost of the mobile application, the available cost estimation method COCOMO II is improved to a new model called New Mobile COCOMO Tool. The New Mobile COCOMO tool developed specifically for mobile applications is a boon for software developers working in small software applications because it only includes important multipliers that play a vital role in estimating the cost of developing mobile applications. Therefore, the objective of this paper is to propose a cost estimation model with a special case of COCOMO II, especially for mobile applications, which calculates the person-month, the programmed time and the average personnel involved in the development of any mobile app.

AN IMPLEMENTATION OF SOFTWARE EFFORT DURATION AND COST ESTIMATION WITH STATISTICAL AND MACHINE LEARNING APPROACHES

In software industry estimation of effort, cost (EDC) and duration is a troublesome procedure. The exertion itself is in charge of experiencing trouble in evaluating EDC. In any software estimation process, the preeminent advance is to characterize and comprehends the framework to be assessed. Software cost estimation algorithmic methods are estimating by analogy, expert judgment method, price to win method, topdown method, bottom-up method are developed by researchers in the field of software engineering. Any methods are not superior to the other methods. In fact, the qualities and shortcomings of those strategies are differentiating to one another. Presently there are two types of software EDC models to estimate the software those are statistical approaches and machine learning approaches. Many of the software cost estimation methods follows statistical approaches, which are not having the capability to present causes and strong or accurate results. Machine learning techniques are suitable in software engineering because it produces accurate results, which estimates by training rules and iterations. Machine learning techniques resolve the challenges like developing programs in a computer and to give the effective outputs by using the experience.

A Modification Complexity Factor in Function Points Method for Software Cost Estimation Towards Public Service Application

Lately, the stages of planning software development projects have begun to consider the scientific side. As an intangible outcome, the budgeting must also be done in a transparent and accountable manner. The authors use Function Points (FP) method approach on the basis of 4 main reasons for estimating the effort and cost of software development (4 public service applications as research object). In this study, there are two core phases, first, elaborating complexity factors based on other method references (i.e Use Case Points) and mapping of non-functional requirements on Term of Reference. Furthermore, the second phase is to calculate and compare the estimated effort and cost if the original FP method before and after modified on the complexity factor. We conclude there is a difference of 7.19 percent (equivalent to IDR 13,567,631) between FP method calculations using and not using modified complexity adjustment factors.

Software Cost Estimation Approaches: A Survey

The software cost estimation aims to predict the most realistic effort that is required to finish a software project and so it is critical to the success of a software project management. A Software Cost Estimation affects nearly all management activities, including project bidding, resource allocation and project planning. It is affected by a number of factors, such as implementation efficiency, as well as how much the various reviews and studies completed prior to the software development stage cost. Accurate cost estimation will help us to complete the project on time and within budget. Accurate estimation is important because it has led to extensive research into the methods of software cost estimation. Some important software cost estimation methods have been studied in this research work. In addition, we have set out own criteria, which has been used to compare all the different selected methods. We have also given a score for each evaluation criteria, so that we can compare the different methods numerically for cost estimation. Our observations have shown that it is best to use a number of different estimating techniques or cost models, and then compare the results before determining the reasons for any of the large variations. None of the methods are necessarily better or worse than the others. We found, in fact, that their strengths and weaknesses often complement each other. Therefore, the main conclusion is that there is no one single technique that is best for every situation, and the results of a number of different approaches need to be carefully considered to discover what is the most likely to produce estimates that are realistic.

Comparative Analysis of Common Software Cost Estimation Modelling Techniques

As software cost estimation in software projects is a very difficult, confusing and challenging work for any software company and software cost estimation is the primary step to start any software project. It gives the overview of resources, efforts and time/schedule required for a software project in respect of cost to the software company. Software project success generally depends on software cost estimation as it provides us an initial idea of the track, challenges and risk involved in the software project development. The software cost estimation in software engineering is very challenging to match the actual cost of the software project with estimated cost. Effective software cost estimation can help software company make more consistent decisions in planning the software project risk. If the predicted estimates are wrong it may lead to negative results for a software company. Many software companies find, search and analyze software project performance by estimating software cost estimation accuracy. Unfortunately, regardless of the large body of experienced and skilled with estimation models, the accuracy of these models is not adequate. In this research paper observation on the performance of the software cost estimation methods and description of methodologies and technique used in to software project cost estimation included. This research paper give comparative comparison study of software cost estimation methods and reviews several classes of software cost estimation models and techniques. Also study the pros and cons of different software cost estimation modeling techniques.

Software Cost Estimation

The development of a successfull software depends upon the accurate estimation, as various factors are responsible for the overall assessment of any project. The Sofware Cost Estimation is the most complicated and challenging task in software industry. Many estimation models are introduced by the time, that concludes estimation is not a precise science and demanding of new methodologies should be proposed day by day. Software Cost Estimation of a project is vital to the acceptance or rejection of the development of software project. Various techniques have been introduced. This paper enlightens two categories of Software Cost Estimation techniques ie Algorithimic and Non Algorithimic. This paper evaluates the Algorithmic Models such as SLIM, COCOMO, Function Points and Non Algorithimic Models such as Analogy, Expert Judgement which are used to estimate software costs. Poor planning frequently leads to project failure and considerable outcomes for the project team. Software project managers should be aware of the increasing of project failures. In this paper, several existing methods for software cost estimation are illustrated and their aspects will be discussed.

The comparison of software cost estimation methods using fuzzy sets theory

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. Software effort estimation is the process of predicting most realistic use of effort required to develop or maintain software based on incomplete and uncertain input. There are various methods suggested by researchers for calculating effort. The best result are achieved by using soft computing technique. In this paper we have represented size in KLOC as a triangular fuzzy number. Fuzzy-based methods compare with common methods. MATLAB is used for tuning the parameters of famous various cost estimation methods. On published software projects data, the performance of the method is evaluated. Comparison of results from SCEFL (Software Cost Estimation using Fuzzy Logic) methods with existing ubiquitous methods is done.

A grey rough set model for evaluation and selection of software cost estimation methods

Purpose – Due to the limitation of acknowledgment, the complexity of software system and the interference of noises, this paper aims to solve the traditional problem: traditional software cost estimation methods face the challenge of poor and uncertain inputs. Design/methodology/approach – Under such circumstances, different cost estimation methods vary greatly on estimation accuracy and effectiveness. Therefore, it is crucial to perform evaluation and selection on estimation methods against a poor information database. This paper presents a grey rough set model by introducing grey system theory into rough set based analysis, aiming for a better choice of software cost estimation method on accuracy and effectiveness. Findings – The results are very encouraging in the sense of comparison among four machine learning techniques and thus indicate it an effective approach to evaluate software cost estimation method where insufficient information is provided. Practical implications – Based on the grey rough set model, the decision targets can be classified approximately. Furthermore, the grey of information and the limitation of cognition can be overcome during the use of the grey rough interval correlation cluster method. Originality/value – This paper proposed the grey rough set model combining grey system theory with rough set for software cost estimation method evaluation and selection.

Emerging Estimation Techniques

Software cost estimation is the process of predicting the effort required to develop a software system. Accurate cost estimation helps us complete the project within time and budget. There are lots of methods are there for efforts and cost estimation, but people do not know how to use these methods. This paper provides a general overview of software cost estimation methods including the recent advances in the respective field. Machine learning techniques such as neural networks, rule induction, genetic algorithm and case-based reasoning are finding application in a wide variety of fields such as computer vision; cloud computing, econometrics and medicine. This paper highlights the cost estimation models that have been proposed and used successfully.

Comparative Analysis of Software Effort Estimation Techniques

Project Failure is the major problem undergoing nowadays as seen by software project managers. Imprecision of the estimation is the reason for this problem. As software grew in size and importance it also grew in complexity, making it very difficult to accurately predict the cost of software development. This was the dilemma in past years. The greatest pitfall of software industry was the fast changing nature of software development which has made it difficult to develop parametric models that yield high accuracy for software development in all domains. Development of useful models that accurately predict the cost of developing a software product. It is a very important objective of software industry. In this paper, several existing methods for software cost estimation are illustrated and their aspects will be discussed. This paper summarizes several classes of software cost estimation models and techniques. To achieve all these goals we implement the simulators. No single technique is best for all situations, and that a careful comparison of the results of several approaches is most likely to produce realistic estimates.

Efficient effort estimation system viz. function points and quality assurance coverage

Software development effort estimation is important for quality management in the software development industry, yet its automation still remains a challenging issue. Accurate estimation of software effort is critical in software engineering. Existing methods for software cost estimation will use very few quality factors for the estimation. So, in order to overcome this drawback, the authors proposed an efficient effort estimation system based on quality assurance coverage. This study is a basis for the improvement of software effort estimation research through a series of quality attributes along with constructive cost model (COCOMO). The classification of software system for which the effort estimation is to be calculated based on COCOMO classes. For this quality assurance ISO 9126 quality factors are used and for the weighing factors the function point metric is used as an estimation approach. Effort is estimated for MS word 2007 using the following models: Albrecht and Gaffney model, Kemerer model, SMPEEM model (Software Maintenance Project Effort Estimation Model and FP Matson, Barnett and Mellichamp model.

A study of project selection and feature weighting for analogy based software cost estimation

A number of software cost estimation methods have been presented in literature over the past decades. Analogy based estimation (ABE), which is essentially a case based reasoning (CBR) approach, is one of the most popular techniques. In order to improve the performance of ABE, many previous studies proposed effective approaches to optimize the weights of the project features (feature weighting) in its similarity function. However, ABE is still criticized for the low prediction accuracy, the large memory requirement, and the expensive computation cost. To alleviate these drawbacks, in this paper we propose the project selection technique for ABE (PSABE) which reduces the whole project base into a small subset that consist only of representative projects. Moreover, PSABE is combined with the feature weighting to form FWPSABE for a further improvement of ABE. The proposed methods are validated on four datasets (two real-world sets and two artificial sets) and compared with conventional ABE, feature weighted ABE (FWABE), and machine learning methods. The promising results indicate that project selection technique could significantly improve analogy based models for software cost estimation.

Software Cost Estimation Method and Application

Software cost estimation has played an important role in software development since its emergence in 1960's. Based on a classification of algorithmic model based methods, non- algorithmic model based methods and composite methods, the typical software cost estimation methods in history are overall reviewed. The issue of software sizing, which is closely related to software cost estimation, is also discussed in this paper. Then a three phases' evaluation criterion of software cost estimation methods is proposed and a case study on cost estimation of government sponsored projects in China is analyzed. At last, six possible trends from estimation models, estimation evolutions, estimation applications, estimation contents, supporting tools and human factors, are presented as a primary conclusion in the paper while viewing the future development for software cost estimation.

Predicting project delivery rates using the Naive–Bayes classifier

AbstractThe importance of accurate estimation of software development effort is well recognized in software engineering. In recent years, machine learning approaches have been studied as possible alternatives to more traditional software cost estimation methods. The objective of this paper is to investigate the utility of the machine learning algorithm known as the Naive–Bayes classifier for estimating software project effort. We present empirical experiments with the Benchmark 6 data set from the International Software Benchmarking Standards Group to estimate project delivery rates and compare the performance of the Naive–Bayes approach to two other machine learning methods—model trees and neural networks. A project delivery rate is defined as the number of effort hours per function point. The approach described is general and can be used to analyse not only software development data but also data on software maintenance and other types of software engineering. The paper demonstrates that the Naive–Bayes classifier has a potential to be used as an alternative machine learning tool for software development effort estimation. Copyright © 2002 John Wiley & Sons, Ltd.