Software Effort Estimation Using Hard Limiting Techniques with Special Reference to Small Size Technical &Analytical Projects

In this paper, we identify a set of factors that may be used to forecast software productivity and software development time. Software productivity was measured in function points per person hours, and software development time was measured in number of elapsed days. Using field data on over 130 field software projects from various industries, we empirically test the impact of team size, integrated computer aided software engineering (ICASE) tools, software development type, software development platform, and programming language type on the software development productivity and development time. Our results indicate that team size, software development type, software development platform, and programming language type significantly impact software development productivity. However, only team size significantly impacts software development time. Our results indicate that effective management of software development teams, and using different management strategies for different software development type environments may improve software development productivity.

Software development involves several interrelated factors that influence development efforts and productivity. Improving the estimation techniques available to project managers will facilitate more effective time and budget control in software development. Software Effort Estimation or software cost/effort estimation can help a software development company to overcome difficulties experienced in estimating software development efforts. This study aims to compare the Machine Learning method of Linear Regression (LR), Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Decision Tree Random Forest (DTRF) to calculate estimated cost/effort software. Then these five approaches will be tested on a dataset of software development projects as many as 10 dataset projects. So that it can produce new knowledge about what machine learning and non-machine learning methods are the most accurate for estimating software business. As well as knowing between the selection between using Particle Swarm Optimization (PSO) for attributes selection and without PSO, which one can increase the accuracy for software business estimation. The data mining algorithm used to calculate the most optimal software effort estimate is the Linear Regression algorithm with an average RMSE value of 1603,024 for the 10 datasets tested. Then using the PSO feature selection can increase the accuracy or reduce the RMSE average value to 1552,999. The result indicates that, compared with the original regression linear model, the accuracy or error rate of software effort estimation has increased by 3.12% by applying PSO feature selection

Software cost estimation is a process of planning, risk analysis, and decision making for project management in software development. Cost of project development encompasses a software project’s effort and development time. One popular model of software cost estimation is constructive cost model (COCOMO) model, which is a mathematical model proposed by Boehm, used for estimate the software effort and development time. The objective of this paper is to improve the basic COCOMO model’s coefficients for modern programming languages like Python, R, C++, etc. Many techniques were presented in the past for effort and time estimation using machine learning. But all these techniques were trained and tested for older programming languages. In order to improve the accuracy of COCOMO for modern programming languages, six Python projects have been considered and genetic algorithm (GA) is applied in these projects to define new values for basic COCOMO coefficients and the development time is calculated for Python projects. The time estimated using GA coefficients is compared with the original COCOMO and actual time. Using mean magnitude relative error, the error from the original COCOMO time is 54.49% and error from GA time is 21.23%.

Software cost and time estimation is the process of estimating the cost and time required to develop a software system. Software cost and time estimation supports the planning and tracking of software projects. Effectively controlling the expensive investment of software development is one of the important issues in software project management. Estimating software development cost with high precision is still a great challenge for project managers, because it allows for considerable financial and strategic planning. Software cost estimation refers to the predictions of the likely amount of effort, time, and staffing levels required to build a software system. A very helpful form of cost estimation is the one made at an early stage during a project, when the costing of the project is proposed for approval. However, estimates at the early stages of the development are the most difficult to obtain. In this paper a novel Constructive Cost Model (COCOMO) based on soft computing approach is proposed for software cost estimation. This model carries some of the desirable features of neural networks approach, such as learning ability and good interpretability, while maintaining the merits of the COCOMO model. Unlike the standard neural networks approach, the proposed model can be interpreted and validated by experts, and has good generalisation capability. The model deals effectively with imprecise and uncertain input and enhances the reliability of software cost estimates. From the experimental results, it was concluded that, by the proposed neural network model, the accuracy of cost estimation can be improved and the estimated cost can be very close to the actual cost.

Software Requirements Changes is a typical phenomenon in any software development project. Restricting incoming changes might cause user dissatisfaction and allowing too many changes might cause delay in project delivery. Moreover, the acceptance or rejection of the change requests become challenging for software project managers when these changes are occurred in Software Development Phase. Where in Software Development Phase software artifacts are not in consistent state such as: some of the class artifacts are Fully Developed, some are Half Developed, some are Major Developed, some are Minor Developed and some are Not Developed yet. However, software effort estimation and change impact analysis are the two most common techniques which might help software project managers in accepting or rejecting change requests during Software Development Phase. The aim of this research is to develop a new software change effort estimation model which helps software project manager in estimating the effort for software Requirement Changes during Software Development Phase. Thus, this research has analyzed the existing effort estimation models and change impact analysis techniques for Softwrae Development Phase from the literature and proposed a new software change effort estimation model by combining change impact analysis technique with effort estimation model. Later, the new proposed model has been evaluated by selecting four small size software projects as case selections in applying experimental approch. The experiment results show that the overall Mean Magnitude Relative Error value produced by the new proposed model is under 25%. Hence it is concluded that the new proposed model is applicable in estimating the amount of effort for requirement changes during SDP.

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Effort estimations are a crucial aspect of software development. The tasks should be completed before the start of any software project. Accurate estimations increase the chances of project success, and inaccurate information can lead to severe issues. This study systematically reviewed the literature on effort-estimating models from 2015-2024, identifying 69 relevant studies from various publications to compile information on various software work estimation models. This review aims to analyze the models proposed in the literature and their classification, the metrics used for accuracy measurement, the leading model that has been chiefly applied for effort estimation, and the benchmark datasets available. The study utilized 542 relevant articles on software development, cost, effort, prediction, estimation, and modelling techniques in the search strategy. After 194 selections, the authors chose 69 articles to understand ML applications in SEE comprehensively. The researchers used a scoring system to assess each study’s responses (from 0 to 5 points) to their research questions. This helped them identify credible studies with higher scores for a comprehensive review aligned with its objectives. The data extraction process identified 91% (63) of 69 studies as either highly or somewhat relevant, demonstrating a successful search strategy for analysis. The literature review on SEE indicates a growing preference for ML-based models in 59% of selected studies. 17% of the studies chosen favor hybrid models to overcome software development challenges. We qualitatively analyzed all the literature on software effort estimation using expert judgment, formal estimation techniques, ML-based techniques, and hybrid techniques. We discovered that researchers have frequently used ML-based models to estimate software effort and are currently in the lead. This study also explores the application of feature importance and selection in machine learning models for Software Effort Estimation (SEE) using popular algorithms like support Vector Machine (SVM), AdaBoost (AB), Gradient Boost (GB), and Random Forest (RF) with six benchmark datasets like CHINA, COCOMO-NASA2, COCOMO, COCOMO81, DESHARNAIS, and KITCHENHAM. We analyze the dataset descriptions and feature importance of the dataset analysis using ML models for choosing crucial play attributes in SEE.

Purpose The study aims to investigate the application of the data element market in software project management, focusing on improving effort estimation by addressing challenges faced by traditional methods. Design/methodology/approach This study proposes a solution based on feature selection, utilizing the data element market and reinforcement learning-based algorithms to enhance the accuracy of software effort estimation. It explores the application of the MARLFS algorithm, customizing improvements to the algorithm and reward function. Findings This study demonstrates that the proposed approach achieves more precise estimation compared to traditional methods, leveraging feature selection to guide project management in software development. Originality/value This study contributes to the field by offering a novel approach that com-bines the data element market, machine learning, and feature selection to improve software effort estimation, addressing limitations of traditional methods and providing insights for future research in project management.

Crowd-Sourced software development (CSSD) is getting a good deal of attention from the software and research community in recent times. One of the key challenges faced by CSSD platforms is the task selection mechanism which in practice, contains no intelligent scheme. Rather, rule-of-thumb or intuition strategies are employed, leading to biasness and subjectivity. Effort considerations on crowdsourced tasks can offer good foundation for task selection criteria but are not much investigated. Software development effort estimation (SDEE) is quite prevalent domain in software engineering but only investigated for in-house development. For open-sourced or crowdsourced platforms, it is rarely explored. Moreover, Machine learning (ML) techniques are overpowering SDEE with a claim to provide more accurate estimation results. This work aims to conjoin ML-based SDEE to analyze development effort measures on CSSD platform. The purpose is to discover development-oriented features for crowdsourced tasks and analyze performance of ML techniques to find best estimation model on CSSD dataset. TopCoder is selected as target CSSD platform for the study. TopCoder’s development tasks data with development-centric features are extracted, leading to statistical, regression and correlation analysis to justify features’ significance. For effort estimation, 10 ML families with 2 respective techniques are applied to get broader aspect of estimation. Five performance metrices (MSE, RMSE, MMRE, MdMRE, Pred (25) and Welch’s statistical test are incorporated to judge the worth of effort estimation model’s performance. Data analysis results show that selected features of TopCoder pertain reasonable model significance, regression, and correlation measures. Findings of ML effort estimation depicted that best results for TopCoder dataset can be acquired by linear, non-linear regression and SVM family models. To conclude, the study identified the most relevant development features for CSSD platform, confirmed by in-depth data analysis. This reflects careful selection of effort estimation features to offer good basis of accurate ML estimate.

Effort estimation is very important for any software development organisation. A lot of work is present for effort estimation in traditional software development but, effort estimation in agile software development (ASD) poses a great research challenge as not much of work is done in this regard. This work proposes a fuzzy approach for effort estimation in ASD environment by identifying first the cost drivers affecting the effort of agile projects and then calculating the effort using fuzzified story point approach. These cost drivers are collected through meetings and by taking a web survey from the companies in Delhi-NCR which practise ASD. Statistical tests are also performed in order to show the dependency of effort on these cost drivers. Here, fuzzy interval type-2 is used which is compared with fuzzy type-1. It is found that, the effort calculated using fuzzy interval type-2 is better than fuzzy type-1.

Estimation of Software Development Effort from Requirements Based Complexity

Prediction of software development is the key task for the effective management of any software industry. The accuracy and reliability of the prediction mechanisms used for the estimation of software development effort is also important. A series of experiments are conducted to gradually progress towards the improved accurate estimation of the software development effort. However, while conducting these experiments, it was found that the size of the training set was not sufficient to train a large and complex artificial neural network (ANN). To overcome the problem of the size of the available training data set, a novel multilayered architecture based on a neural network model is proposed. The accuracy of the proposed multi-layered model is assessed using different criteria, which proves the pre-eminence of the proposed model.

Inconsistency of expert judgment-based estimates of software development effort

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.

Although the age of software engineering goes more than a half of a century, successful software projects which meet time and costs based on primary estimation are also in high risk. There are various reasons with different weights for that. Management problem, scheduling and effort estimation are the most important ones. Precise estimation for software development is a very hard and complex task. Because of software special problems in Iran estimation challenges have been doubled between software development companies. Based on our experience at work, many incorrect estimations in software companies urged us research about more accurate statistics and reasons for this problem. More than thirty known companies were selected. We prepared a questionnaire to collect data about companies, as well as, their projects and information about project estimation. The results were analyzed and showed that in most of the cases estimation deviation is more than 40% from primary estimation and in some cases it is up to 60%. These statistics show the necessity of traumatology and more investigation into reasons of these deviations. Our studies have specified the most important reasons of these deviations and suggest some approaches to control them.