Software Cost Estimation for Python Projects Using Genetic Algorithm

The software cost estimation (SCE) problem is one of the major challenges in software engineering. Inaccurate cost and time estimation in a software project may lead to devastating damage to software companies. To deal with this issue, software researchers have made significant efforts during recent years to improve and modify the available SCE models, one widely-used model of which is the Constructive Cost Model (COCOMO). This research aims to optimize the coefficients of a standard COCOMO model for SCE by combining genetic algorithm (GA) and environmental adaptation (EA) methods. The results indicate that the EA algorithm can solve the divergence issue of the genetic algorithm and optimize the coefficients of the COCOMO model as well. Moreover, the accuracy of the SCE in the case of combining GA and EA algorithms is 8% higher than when these algorithms are separately adopted.

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.

Playing a pivotal role in software development, the Constructive Cost Model (COCOMO) offers a systematic and structured approach to cost estimation. It stands as a widely utilized model, aiding project managers in estimating the required effort, time, and cost for software development projects. COCOMO takes into consideration diverse factors, including the project's size, complexity, and the experience of the development team. The utilization of COCOMO empowers software development teams to make informed decisions related to resource allocation, project scheduling, and budgeting. Its application extends to managing expectations, enhancing project planning, and mitigating the risk of cost overruns. The integration of machine learning assumes a critical role in advancing cost estimation within the realm of software development, specifically through COCOMO. Through the utilization of machine learning algorithms, COCOMO gains the capability to analyze and interpret extensive datasets, taking into account numerous complex factors that influence project costs. This work aims to propose a model for software cost estimation using an advanced machine learning technique, i.e. Adaptive boosting, which has improved accuracy, reduced overfitting, effectiveness with imbalanced data, and good generalization capabilities. The proposed work may contribute to the success of software projects, providing a reliable and comprehensive framework for cost estimation.

Software development effort estimates have several major sources of uncertainty. Among these uncertainties are the size of the project, the development attribute ratings, and the error of the estimation model. This paper presents three methods which quantify the effects of these uncertainties on development effort estimates. One method takes advantage of the invertibility of the nonlinear effort models to approximate the effort probability distribution. In the case of a single software configuration item, this methods yields the exact probability distribution. A second method uses Taylor series to estimate mean and variance of effort, and then specifies its probability distribution by invoking the Central Limit Theorem. The third method, specific to the Constructive Cost Model (COCOMO), invokes a Monte Carlo simulation technique to approximate the effort probability distribution. The results of case studies based on the COCOMO model are presented and compared. The mathematical details are provided so that analysts may easily review and implement these methods within their organizations.

Estimating effort is a very important task in any organization. Significant over or under-estimates can be very expensive for software project companies. The use of computing intelligence methods has been recently proposed for software development effort estimation. In this study, we present new models to estimate the effort required for the development of software projects. These new models were calculated using Linear Genetic Programming (PGL). The results show that the proposed models get more precise and more effective estimation for Mean Magnitude Relative error (MMRE) and Mean Magnitude of Relative Error relative to the Estimate (MMER) than using the constructive cost model (COCOMO). We performed the study based on three stages according to the type of project. The models were designed and validated by simulation with the public repository dataset COCOMO81 and NASA93. Performance of the proposed models EE_PGLa, EE_PGLb and EE_PGLc are more accurate than COCOMO.

Project organization and management software development project management model is the best form of software development enterprises in China's understanding of software development project management has been greatly improved. Traditional software development projects have different characteristics, therefore how flexible application for software project management theory and strategies of the main research objectives of domestic software development project management. Our software in enterprise application software development project management, general shortcomings in implementation, this article on software research and development analysis and project management of implementation strategies.

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.

Software development effort estimation (SDEE) is a critical activity in developing software. Accurate effort estimation in the early phases of software design life cycle has important effects on the success of software projects. COCOMO (Constructive Cost Model) is a parametric data‐driven SDEE model whose parameters must be calibrated with an organization's local data for accurate estimation. Such data are scarce for most organizations. On the other hand, CoBRA (Cost estimation, Benchmarking, and Risk Assessment) is one of the powerful hybrid methods that need a small number of local historical data for effort estimation. However, data gathering in CoBRA is time‐consuming and costly. To ease the use of CoBRA, in this paper, we design a methodology that extracts CoBRA‐required data from COCOMO datasets. By the proposed method, data collected for COCOMO would be used in CoBRA. Using CoBRA, a more accurate estimation of the required effort would be achieved with fewer number of historical data than what is required to calibrate the COCOMO model. We apply the proposed method on six well‐known public COCOMO datasets and use them in CoBRA. Obtained results depict an increase in the accuracy of estimations in comparison with other existing methods.

The web projects that are completed on time and within budget ascertain a commendable position in the rapidly growing economic web development market.Web Effort Estimation (WEE) estimates the time t will take to develop a web application in person-hours or months Expert Opinion algorithmic models, e.g., Constructive Cost Model (COCOMO), and machine learning are the primarily used effort estimating techniques.As current effort estimating techniques face many shortcomings, accurate effort prediction has become a challenging task.To improve prediction accuracy, this work proposes a hybrid approach based on Machine Learning.This approach is validated through an empirical evaluation of the International Software Benchmark Software Group, ISBSG Release 19 dataset.The ISBSG R19 dataset is first pre-processed using machine learning-based linear regression.Secondly, Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest regression (RFR), and Ridge Regression (RR) techniques are employed to predict the web effort.The performance of the examined models is evaluated using two commonly used evaluation metrics, Mean Magnitude Relative Error (MMRE) and Prediction accuracy at level 25%, i.e., Pred(25).Then, the statistical significance of effort predicting model producing the highest accuracy and lowest error rates is verified using the Mann-Whitney U test.The performance of the proposed models is also compared with the existing effort estimation models.The results show that the Ridge regressionbased model produces exceptionally improved prediction accuracy for web projects in this work.

The Project Management in the area of IT is becoming hot topic now. The risk problems in IT Projects, especially in software projects become more and more concentration in software project management in China. This paper analyze systematically the characteristics of project management for software itself, then the paper analyses the risk of software development project in following two aspects: one for owners another for contractors. To owners the paper identify and analyze the risk in software development projects according to life cycle of a project. To contractors the paper identifies and assess the risk of project management in software development, and on the bases of investigating of software development project in the IT enterprises of China some conclusions are obtained with the method of AHP.

Differential evolution using homeostasis adaption based mutation operator and its application for software cost estimation

An empirical validation of a neural network model for software effort estimation

The management of software development for single and dual processor system designs is making progress towards becoming a mature discipline. A good part of the progress can be attributed to the development and use of standard system and software engineering methods and design principles.However, new computer system designs (networking, distributed systems, embedded systems, multi- and coprocessors, fault tolerant systems, etc.) will create new challenges for managers of software development. The reason for this effect on management is that some of the system and software engineering methods and design principles developed for single and dual processor system designs are not valid for these newer designs.Some of the issues that software development project managers will need to cope with are:1. Life cycle model adjustments2. Rapid prototyping activities3. Different hardware and software phasing4. Increased tool development5. New trade-offs and hybrid developments of off-the-shelf software and newly developed software6. Development of concurrent design principles7. New software design principles to support fault tolerance and the use of new memory technologiesThe above items are just now being recognized as problems, and solutions for them either do not exist, or are not widely known.These problems create a series of new challenges that managers must deal with for software development based on the new architectures and requirements. The purposes of this paper are to discuss these issues and to identify some solutions that can serve in the interim as the technology changes to meet these new challenges.

Managing software development and maintenance projects requires early knowledge about quality and effort needed for achieving a necessary quality level. Quality prediction models can identify outlying software components that might cause potential quality problems. Quality prediction is based on experience with similar predecessor projects constructing a relationship between the output-usually the number of errors-and some kind of input-here we use complexity metrics-to the quality of a software development project. Two approaches are presented to build quality prediction models: multilinear discriminant analysis as one example for conventional approaches and fuzzy expert-systems generated by genetic algorithms. Using the capability of genetic algorithms, the fuzzy rules can be automatically generated from example data to reduce the cost and improve the accuracy. The generated quality model-with respect to changes-provides both quality of fit (according to past data) and predictive accuracy (according to ongoing projects). The comparison of the approaches gives an answer on the effectiveness and the efficiency of a soft-computing approach.

Software cost and effort estimation using dragonfly whale optimized multilayer perceptron neural network