Accurate Software Effort Estimation Research Articles

Framework to improve software effort estimation accuracy using novel ensemble rule

This investigation focuses on refining software effort estimation (SEE) to enhance project outcomes amidst the rapid evolution of the software industry. Accurate estimation is a cornerstone of project success, crucial for avoiding budget overruns and minimizing the risk of project failures. The framework proposed in this article addresses three significant issues that are critical for accurate estimation: dealing with missing or inadequate data, selecting key features, and improving the software effort model. Our proposed framework incorporates three methods: the Novel Incomplete Value Imputation Model (NIVIM), a hybrid model using Correlation-based Feature Selection with a meta-heuristic algorithm (CFS-Meta), and the Heterogeneous Ensemble Model (HEM). The combined framework synergistically enhances the robustness and accuracy of SEE by effectively handling missing data, optimizing feature selection, and integrating diverse predictive models for superior performance across varying project scenarios. The framework significantly reduces imputation and feature selection overhead, while the ensemble approach optimizes model performance through dynamic weighting and meta-learning. This results in lower mean absolute error (MAE) and reduced computational complexity, making it more effective for diverse software datasets. NIVIM is engineered to address incomplete datasets prevalent in SEE. By integrating a synthetic data methodology through a Variational Auto-Encoder (VAE), the model incorporates both contextual relevance and intrinsic project features, significantly enhancing estimation precision. Comparative analyses reveal that NIVIM surpasses existing models such as VAE, GAIN, K-NN, and MICE, achieving statistically significant improvements across six benchmark datasets, with average RMSE improvements ranging from 11.05% to 17.72% and MAE improvements from 9.62% to 21.96%. Our proposed method, CFS-Meta, balances global optimization with local search techniques, substantially enhancing predictive capabilities. The proposed CFS-Meta model was compared to single and hybrid feature selection models to assess its efficiency, demonstrating up to a 25.61% reduction in MSE. Additionally, the proposed CFS-Meta achieves a 10% (MAE) improvement against the hybrid PSO-SA model, an 11.38% (MAE) improvement compared to the Hybrid ABC-SA model, and 12.42% and 12.703% (MAE) improvements compared to the hybrid Tabu-GA and hybrid ACO-COA models, respectively. Our third method proposes an ensemble effort estimation (EEE) model that amalgamates diverse standalone models through a Dynamic Weight Adjustment-stacked combination (DWSC) rule. Tested against international benchmarks and industry datasets, the HEM method has improved the standalone model by an average of 21.8% (Pred()) and the homogeneous ensemble model by 15% (Pred()). This comprehensive methodology underscores our model’s contributions to advancing software project management (SPM) through advanced predictive modeling, setting a new benchmark for software engineering effort estimation.

Retraction Note: Enrichment of accurate software effort estimation using fuzzy-based function point analysis in business data analytics

Retraction Note: Enrichment of accurate software effort estimation using fuzzy-based function point analysis in business data analytics

Selecting best predictors from large software repositories for highly accurate software effort estimation

AbstractAccurate prediction of software effort is important for planning, scheduling, and allocating resources. However, software effort estimation has been a challenging task. Although numerous estimation models have been proposed, few achieve anything close to accurate prediction of software development effort. To achieve optimal results, machine learning techniques have recently been employed for predicting software development effort using relatively large software repositories. However, some issues remain unresolved, and this paper aims to address the following issues. First, feature selection methods often neglected the information rich variables present in the dataset. Second, selection of important features was done through statistical methods, which lack domain knowledge. Third, missing values in the data that significantly influence the prediction outcome was not efficiently handled. Fourth, majority of the literature neglected advanced evaluation measures, which thoroughly evaluate the ability of learning models to produce accurate results. To address the above issues, a machine learning‐based model has been proposed in this paper, which not only allows effective preprocessing of data but also provides highly accurate prediction results with minimum error rate. The purpose is to best identify attributes (predictors) from large software repositories that are most influential in the estimation of effort. In addition, we apply MMRE for better performance analysis.

A systematic review of studies on use case points and expert‐based estimation of software development effort

AbstractIn recent years, due to significant evolution in adopting new technologies and development methodologies in the field of software engineering, the developers and researchers are striving to optimize the accuracy of software effort estimation (SEE). The overestimation and underestimation both are the key challenges for software progress; hence, there is a continuous need for an accurate SEE. This paper highlights a systematic review of studies associated with the best practices of use case point (UCP) and expert judgment–based software development effort estimation techniques. The primary aim and contribution of this paper are to support the researchers through an extensive review to ease to other researcher's search for effort estimation studies. We have performed state‐of‐the‐art review from five viewpoints of reference: (a) review of studies concerning UCPs and expert judgment–based effort estimation, (b) research contribution and future recommendation in different novelties, (c) usage of the dataset, (d) availability of accuracy metrics, and (e) findings of the studies. We have performed a systematic review of studies which are published in the period of 2000 to 2019. We have selected a total of 34 primary studies of UCP and expert judgment–based estimation techniques to report the research questions stated in this review.

Software Cost Estimation Based on Dolphin Algorithm

Accurate Software Effort Estimation is of high importance with regard to Software Project Management. It can be specified as the process for predicting Effort regarding costs, needed for developing software products. A lot of techniques related to software effort estimation were carried out for developing models that are generating optimal estimation accuracy. Swarm intelligence is one such technique. The process-related in selecting the optimum estimation algorithm is expert dependent and complex. The presented study optimizes the estimation using the COCOMO II models by two models: the first model applied the dolphin algorithm, the second model applied suggested hybrid dolphin and bat algorithm (DolBat). By applying the two models on two data set and evaluate with the use of Magnitude of Relative Error(MRE) and Mean Magnitude of Relative Error(MMRE). The results indicate that the dolphin algorithm has better than previous algorithms but the (DolBat) is the best to get the coefficient value of the COCOMO II model.

RETRACTED ARTICLE: Enrichment of accurate software effort estimation using fuzzy-based function point analysis in business data analytics

Accurate effort estimation is a significant task in software development, which is helpful in the scheduling and tracking of the project. A number of estimation models are available for effort calculation. However, a lot of newer models are still being proposed to obtain more accurate estimation. This paper attempts to propose a hybrid technique which incorporates both quality factors and fuzzy-based technique in function point analysis. Fuzzy logic has the capability of tackling the uncertainty issues in the estimation. The goal of this paper is to evaluate the accuracy of fuzzy analysis for software effort estimation. In this approach, fuzzy logic is used to control the uncertainty in the software size with the help of a triangular fuzzy set, and defuzzification through the weighted average method. The experimentation is done with different project data on the proposed model, and the results are tabulated. The measured effort of the proposed model is compared with that of the existing model, and finally, the performance evaluation is done based on parameters in terms of MMRE and VAF.

Cuckoo search based hybrid models for improving the accuracy of software effort estimation

This research proposes a new approach which is based on Cuckoo Search algorithm for the prediction of software development effort. It uses Cuckoo Search for discovering the best possible parameters of COCOMO II model and then further hybridizes with ANN for increasing the accuracy to better predict the software development effort. The proposed hybrid models have been tested on two standard datasets. During experimentation, it has been seen that the proposed hybrid models provide more accurate and effective results than other existing models. The result has been analyzed with MMRE and three different types of PRED 25, 30 and 40% that shows the efficiency and capability of the proposed hybrid models. A comparative study of computational complexity with other existing approach has also been done which shows the superiority of the proposed model over existing approaches.

OPTIMIZED MODEL FOR SOFTWARE EFFORT ESTIMATION USING COCOMO-2 METRICS WITH FUZZY LOGIC

Effort estimation is the crucial activity during the planning phase of any project. Successful delivery of the software project directly dependent on the accuracy of software effort estimation in planning phase. As effort multiplier have significant influence on the COCOMO-II and this research proposed the model for improving the precision of effort estimation using fuzzy logic on COCOMO-II effort multipliers. Fuzzy Logic is a rule based architecture which runs on binary pattern. It has Input set, associated with rule-sets based on the membership function. There are three membership functions i.e. Triangular Membership function, Trapezoidal Membership Function and Bell Membership Function which has been utilized in the proposed architecture.

Class point approach for software effort estimation using stochastic gradient boosting technique

The success of software development depends on the proper estimation of the effort required to develop the software. Project managers require a reliable approach for software effort estimation. It is especially important during the early stages of the software development life cycle. Accurate software effort estimation is a major concern in software industries. Stochastic Gradient Boosting (SGB) is one of the machine learning techniques that helps in getting improved estimated values. SGB is used for improving the accuracy of models built on decision trees. In this paper, the main goal is to estimate the effort required to develop various software projects using the class point approach. Then, optimization of the effort parameters is achieved using the SGB technique to obtain better prediction accuracy. Further- more, performance comparisons of the models obtained using the SGB technique with the Multi Layer Perceptron and the Radial Basis Function Network are presented in order to highlight the performance achieved by each method.

Software Effort Estimation Using Multilayer Perceptron and Adaptive Neuro Fuzzy Inference System

Accurate software effort estimation has a big importance for software companies for the reason that management of the project, control of the project, financial matters and timely deliveries are achieved with effort estimation. Thus, effort estimation plays vital role for software companies. In this study, software effort estimation is predicted by using Multilayer Perceptron and Adaptive Neuro Fuzzy Inference System. As a dataset, NASA 93 with 93 projects and Desharnais with 77 projects are used. The results show that Mean Magnitude Relative Error of Adaptive Neuro Fuzzy Inference System is lower than Multilayer Perceptron. In addition, it is seen that PRED(0.25) value of Adaptive Neuro Fuzzy Inference System is higher than Multilayer Perceptron. Thus, performance of Adaptive Neuro Fuzzy Inference System is better when compared to performance of Multilayer Perceptron.

To Design and Implement Neural Network and Fuzzy Logic for Software Development Effort Prediction

One of the greatest challenges for software developers is forecasting the development effort for a software system for the last decades. The capability to provide a good estimation on software development efforts is necessitated by the project managers. Software effort estimation model divided into two main categories: algorithmic and nonalgorithmic. These models too have difficulty in modeling the inherent complex relationships between the contributing factors, are unable to handle categorical data as well as lack of reasoning capabilities. The limitations of these models led to the exploration of the techniques which are soft computing based. In This paper we have compared neural network and fuzzy logic model for software development effort estimation. It will help us to make accurate software effort estimation by these estimation techniques General Terms Fuzzy logic, neural network, Software effort estimation

Software Effort Estimation Using a Neural Network Ensemble

Accurate software effort estimation is crucial for software consulting organizations to stay competitive in their software development costs and retain customers. Artificial Neural Network (ANN) is an effective tool to obtain accurate effort estimates. In this paper, software effort estimation models using Artificial Neural Network (ANN) ensembles and regression analysis are developed based on data collected from 163 software development projects. The main emphasis of the paper is in developing an effective experimental design to achieve superior effort estimation results. In addition, we compare the software effort estimation of ANNs and multiple regression analysis. We found two interesting results. First, variables other than size (function points) are not especially helpful in predicting software development effort. Second, a properly designed ANN ensemble significantly outperforms estimation using regression analysis and can achieve better effort estimate predictions.

Software Effort Prediction using Statistical and Machine Learning Methods

Accurate software effort estimation is an important part of software process. Effort is measured in terms of person months and duration. Both overestimation and underestimation of software effort may lead to risky consequences. Also, software project managers have to make estimates of how much a software development is going to cost. The dominant cost for any software is the cost of calculating effort. Thus, effort estimation is very crucial and there is always a need to improve its accuracy as much as possible. There are various effort estimation models, but it is difficult to determine which model gives more accurate estimation on which dataset. This paper empirically evaluates and compares the potential of Linear Regression, Artificial Neural Network, Decision Tree, Support Vector Machine and Bagging on software project dataset. The dataset is obtained from 499 projects. The results show that Mean Magnitude Relative error of decision tree method is only 17.06%. Thus, the performance of decision tree method is better than all the other compared methods.

A Novel Algorithmic Cost Estimation Model Based on Soft Computing Technique

Problem statement: Software development effort estimation is the process of predicting the most realistic use of effort required for developing software based on some parameters. It has always characterized one of the biggest challenges in Computer Science for the last decades. Because time and cost estimate at the early stages of the software development are the most difficult to obtain and they are often the least accurate. Traditional algorithmic techniques such as regression models, Software Life Cycle Management (SLIM), COCOMO II model and function points, require an estimation process in a long term. But, nowadays that is not acceptable for software developers and companies. Newer soft computing techniques to effort estimation based on non-algorithmic techniques such as Fuzzy Logic (FL) may offer an alternative for solving the problem. This work aims to propose a new fuzzy logic realistic model to achieve more accuracy in software effort estimation. The main objective of this research was to investigate the role of fuzzy logic technique in improving the effort estimation accuracy by characterizing inputs parameters using two-side Gaussian function which gave superior transition from one interval to another. Approach: The methodology adopted in this study was use of fuzzy logic approach rather than classical intervals in the COCOMO II. Using advantages of fuzzy logic such as fuzzy sets, inputs parameters can be specified by distribution of its possible values and these fuzzy sets were represented by membership functions. In this study to get a smoother transition in the membership function for input parameters, its associated linguistic values were represented by two-side Gaussian Membership Functions (2-D GMF) and rules. Results: After analyzing the results attained by means of applying COCOMO II and proposed model based on fuzzy logic to the NASA dataset and created an artificial dataset, it had been found that proposed model was performing better than ordinal COCOMO II and the achieved results were closer to the actual effort. The relative error for proposed model using two-side Gaussian membership functions is lower than that of the error obtained using ordinal COCOMO II. Conclusion: Based on the achieved results, it was concluded that, using soft computation approaches such as fuzzy logic and their advantages, good predication; adaption; understandability and the accuracy of software effort estimation can be improved and the estimation can be very close to the actual effort. This novelty model will lead researchers to focus on benefits of non-algorithmic models to overcome the estimation problems.

Can genetic programming improve software effort estimation? A comparative evaluation

Accurate software effort estimation is an important part of the software process. Originally, estimation was performed using only human expertise, but more recently, attention has turned to a variety of machine learning (ML) methods. This paper attempts to evaluate critically the potential of genetic programming (GP) in software effort estimation when compared with previously published approaches, in terms of accuracy and ease of use. The comparison is based on the well-known Desharnais data set of 81 software projects derived from a Canadian software house in the late 1980s. The input variables are restricted to those available from the specification stage and significant effort is put into the GP and all of the other solution strategies to offer a realistic and fair comparison. There is evidence that GP can offer significant improvements in accuracy but this depends on the measure and interpretation of accuracy used. GP has the potential to be a valid additional tool for software effort estimation but set up and running effort is high and interpretation difficult, as it is for any complex meta-heuristic technique.

An empirical study using task assignment patterns to improve the accuracy of software effort estimation

In most software development organizations, there is seldom a one-to-one mapping between software developers and development tasks. It is frequently necessary to concurrently assign individuals to multiple tasks and to assign more than one individual to work cooperatively on a single task. A principal goal in making such assignments should be to minimize the effort required to complete each task. But what impact does the manner in which developers are assigned to tasks have on the effort requirements? This paper identifies four task assignment factors: team size, concurrency, intensity, and fragmentation. These four factors are shown to improve the predictive ability of the well-known intermediate COCOMO cost estimation model. A parsimonious effort estimation model is also derived that utilizes a subset of the task assignment factors and unadjusted function points. For the data examined, this parsimonious model is shown to have goodness of fit and quality of estimation superior to that of the COCOMO model, while utilizing fewer cost factors.