Software Effort Estimation Models Research Articles

Novel Approach to Design a Model for Software Effort Estimation Using Linear Regression

Estimation of an effort is necessary to decide the time and cost needed for the completion of the project. Making judgments with a high degree of uncertainty is a serious issue in the field of software engineering. Software quality forecasting calls for highly precise technologies and expert knowledge. The forecast of software effort based on past data from software development metrics could, instead, be aided by AI-based predictive models, which have a high degree of accuracy. Using a linear regression model, we developed a software effort estimation model in this study to forecast this effort. A non-parametric linear regression approach based on K-Nearest Neighbors was used to create this statistical model (KNN). So, with a 76% coefficient of determination, our findings indicate the potential for applying AI algorithms to predict the software engineering work prediction problem.

A random forest model for early-stage software effort estimation for the SEERA dataset

ContextPublicly available software cost estimation datasets are outdated and may not represent current industrial environments. Thus most research has concentrated on the development and evaluation of estimation models with limited evidence of their applicability to industrial practice. Moreover, these datasets and models may not be applicable in (under-represented) technically and economically constrained environments such as the software development environment in Sudan. ObjectiveThis paper aims to develop a machine learning model that is suitable for the Sudanese software industry. To demonstrate the suitability of our approach, we evaluate our model using the publicly available SEERA (Software enginEERing in SudAn) dataset, which is a software cost estimation dataset from organizations in Sudan. MethodWe demonstrated the suitability of the SEERA dataset for effort estimation by comparing the attributes that had a high correlation with actual effort and actual duration to the cost factors identified by (Sudanese) experts. In addition, we developed an early-stage Random Forest model to estimate project effort and duration from the SEERA dataset. Early-stage estimation is in-line with current Sudanese industrial practice. We investigated the impact of oversampling, feature selection, heterogeneity and local environmental factors on model accuracy. ResultsOur experimental results showed that the Random Forest model with oversampling and feature selection provided accurate estimates that were better than random guessing (standardized accuracy > 70 %). Our results were similar to accuracies reported in the literature. In addition, we demonstrated that our random forest model provided estimations that were more accurate than (Sudanese) expert judgement. ConclusionThis study has demonstrated the feasibility of our random forest model for early-stage effort and duration estimation for Sudanese software projects. The results demonstrate the importance of representative models and datasets for non-traditional technical environments. Further research is required to investigate the impact of local environmental factors on software cost estimation.

Software Effort Estimation through Ensembling of Base Models in Machine Learning using a Voting Estimator

For a long time, researchers have been working to predict the effort of software development with the help of various machine learning algorithms. These algorithms are known for better understanding the underlying facts inside the data and improving the prediction rate than conventional approaches such as line of code and functional point approaches. According to no free lunch theory, there is no single algorithm which gives better predictions on all the datasets. To remove this bias our work aims to provide a better model for software effort estimation and thereby reduce the distance between the actual and predicted effort for future projects. The authors proposed an ensembling of regressor models using voting estimator for better predictions to reduce the error rate to over the biasness provide by single machine learning algorithm. The results obtained show that the ensemble models were better than those from the single models used on different datasets.

Early size estimation of web apps created using codeigniter framework by nonlinear regression models

Subject matter: Early software size estimation is one of the project managers' significant problems in evaluating app development efforts because software size is the major determinant of software project effort. Function points (FPs) and lines of code (LOC) are most commonly used as measures of size in existing software effort estimation methods and models. As is known, both these metrics have their advantages and disadvantages when used for software effort estimation. Although the FPs-based measure has the advantage over the LOC in that it does not depend on the technologies used, however, the assessment of efforts requires considering such factors (environmental factors). Considering the above factors can be ensured by appropriate models for estimating the LOC-based effort. Nowadays, many Web apps are created using PHP frameworks making the app development faster. CodeIgniter is one such powerful framework. However, there are no regression models for estimating the software size of Web apps created using the CodeIgniter framework. This requires the construction of the appropriate models. The task of this paper is to develop a nonlinear regression model for estimating the software size (in KLOC, kilo lines of code) of Web apps created using the CodeIgniter framework. Method: We apply the technique for constructing nonlinear regression models based on the multivariate normalizing transformations and prediction intervals. The result is three nonlinear regression models with three predictors: the total number of classes, the average number of methods per class, and the DIT (Depth of Inheritance Tree) average per class. To build these models for estimating the size of Web apps created using the CodeIgniter framework, we used three well-known normalizing transformations: two univariate transformations (the decimal logarithm and the Box-Cox transformation) and the Box-Cox four-variate transformation. Conclusions. The nonlinear regression model constructed by the Box-Cox four-variate transformation has better size prediction results than other regression models based on the univariate transformations.

Retraction Note to: Hyperparameters tuning of ensemble model for software effort estimation

Retraction Note to: Hyperparameters tuning of ensemble model for software effort estimation

Parametric Software Effort Estimation Based on Optimizing Correction Factors and Multiple Linear Regression

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context:</i> Effort estimation is one of the essential phases that must be accurately predicted in the early stage of software project development. Currently, solving problems that affect the estimation accuracy of Use Case Points-based methods is still a challenge to be addressed. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> This paper proposes a parametric software effort estimation model based on Optimizing Correction Factors and Multiple Regression Models to minimize the estimation error and the influence of unsystematic noise, which has not been considered in previous studies. The proposed method takes advantage of the Least Squared Regression models and Multiple Linear Regression models on the Use Case Points-based elements. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method:</i> We have conducted experimental research to evaluate the estimation accuracy of the proposed method and compare it with three previous related methods, i.e., 1) the baseline estimation method – Use Case Points, 2) Optimizing Correction Factors, and 3) Algorithmic Optimization Method. Experiments were performed on datasets (Dataset D1, Dataset D2, and Dataset D3). The estimation accuracy of the methods was analysed by applying various unbiased evaluation criteria and statistical tests. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> The results proved that the proposed method outperformed the other methods in improving estimation accuracy. Statistically, the results proved to be significantly superior to the three compared methods based on all tested datasets. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> Based on our obtained results, the proposed method has a high estimation capability and is considered a helpful method for project managers during the estimation phase. The correction factors are considered in the estimation process.

Fuzzy multi-attribute decision-making approach for the selection of software effort estimation models

Fuzzy multi-attribute decision-making approach for the selection of software effort estimation models

Optimized COCOMO parameters using hybrid particle swarm optimization

Software effort and cost estimation are crucial parts of software project development. It determines the budget, time, and resources needed to develop a software project. The success of a software project development depends mainly on the accuracy of software effort and cost estimation. A poor estimation will impact the result, which worsens the project management. Various software effort estimation model has been introduced to resolve this problem. COnstructive COst MOdel (COCOMO) is a well-established software project estimation model; however, it lacks accuracy in effort and cost estimation, especially for current projects. Inaccuracy and complexity in the estimated effort have made it difficult to efficiently and effectively develop software, affecting the schedule, cost, and uncertain estimation directly. In this paper, Particle Swarm Optimization (PSO) is proposed as a metaheuristics optimization method to hybrid with three traditional state-of-art techniques such as Support Vector Machine (SVM), Linear Regression (LR), and Random Forest (RF) for optimizing the parameters of COCOMO models. The proposed approach is applied to the NASA software project dataset downloaded from the promise repository. Comparing the proposed approach has been made with the three traditional algorithms; however, the obtained results confirm low accuracy before hybrid with PSO. Overall, the results showed that PSOSVM on the NASA software project dataset could improve effort estimation accuracy and outperform other models.

Metaheuristic Algorithms in Optimizing Deep Neural Network Model for Software Effort Estimation

Effort estimation is the most critical activity for the success of overall solution delivery in software engineering projects. In this context, the paper’s main contributions to the literature on software effort estimation are twofold. First, this paper examines the application of meta-heuristic algorithms to have a logical and acceptable parametric model for software effort estimation. Secondly, to unravel the benefits of nature-inspired meta-heuristic algorithms usage in optimizing Deep Learning (DL) architectures for software effort estimation, this paper presents a Deep Neural Network (DNN) model for software effort estimation based on meta-heuristic algorithms. In this paper, Grey Wolf Optimizer (GWO) and StrawBerry (SB) meta-heuristic algorithms are applied for having a logical and acceptable parametric model for software effort estimation. To validate the performances of these two algorithms, a set of nine benchmark functions having wide dimensions is applied. Results from GWO and SB algorithms are compared with five other meta-heuristic algorithms used in literature for software effort estimation. Experimental results showed that the GWO has comprehensive superiority in terms of accuracy in estimation. The proposed DNN model (GWDNNSB) using meta-heuristic algorithms for initial weights and learning rate selection, produced better results compared to existing work on using DNN for software effort estimation.

AGS: a precise and efficient AI-based hybrid software effort estimation model

To predict the amount of effort to develop software is a tedious process for software companies. Hence, predicting the software development effort remains a complex issue drawing in extensive research consideration. The success of software development process considerably depends on proper estimation of effort required to develop that software. Effective software effort estimation techniques enable project managers to schedule software life cycle activities properly. The main objective of this paper is to propose a novel approach in which an artificial intelligence (AI)-based technique, called AGS algorithm, is used to determine the software effort estimation. AGS is hybrid method combining three techniques, namely: adaptive neuro fuzzy inference system (ANFIS), genetic algorithm and satin bower bird optimisation (SBO) algorithm. The performance of the proposed method is assessed using a well standard dataset with real-time benchmark with many attributes. The major metrics used in the performance evaluation are correlation coefficient (CC), kilo lines of code (KLoC) and complexity of the software. The experimental result shows that the prediction accuracy of the proposed model is better than the existing algorithmic models.

Analyzing the Stationarity Process in Software Effort Estimation Datasets

Software effort estimation models are typically developed based on an underlying assumption that all data points are equally relevant to the prediction of effort for future projects. The dynamic nature of several aspects of the software engineering process could mean that this assumption does not hold in at least some cases. This study employs three kernel estimator functions to test the stationarity assumption in five software engineering datasets that have been used in the construction of software effort estimation models. The kernel estimators are used in the generation of nonuniform weights which are subsequently employed in weighted linear regression modeling. In each model, older projects are assigned smaller weights while the more recently completed projects are assigned larger weights, to reflect their potentially greater relevance to present or future projects that need to be estimated. Prediction errors are compared to those obtained from uniform models. Our results indicate that, for the datasets that exhibit underlying nonstationary processes, uniform models are more accurate than the nonuniform models; that is, models based on kernel estimator functions are worse than the models where no weighting was applied. In contrast, the accuracies of uniform and nonuniform models for datasets that exhibited stationary processes were essentially equivalent. Our analysis indicates that as the heterogeneity of a dataset increases, the effect of stationarity is overridden. The results of our study also confirm prior findings that the accuracy of effort estimation models is independent of the type of kernel estimator function used in model development.

RETRACTED ARTICLE: Hyperparameters tuning of ensemble model for software effort estimation

This article presents an effective method to improve estimation accuracy of software projects to a significant level by tuning the hyperparameters of the stacking ensemble model using evolutionary methods. Traditional and parametric methods for software effort estimation are mostly inaccurate due to bias and subjectivity. Machine Learning methods are found to be effective in dealing with bias and subjectivity issues, if the data is subjected to appropriate data pre-processing and feature extraction methods. Instead of employing a single machine learning model to estimate the software project effort, ensemble of learning models is deployed to improve the estimate. Accurate hyperparameters need to be determined to operate the ensemble model at optimised level and to reduce the errors. Hyperparameters setting is traditionally done manually according to the problem and dataset by trial and error, which is a cumbersome process. In this paper, two evolutionary approaches namely Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) have been employed to tune the hyperparameters. ISBSG dataset has been used for constructing the stacking ensemble model, which is a heterogeneous dataset consisting of software project data from different countries and organizations. Experimental outcomes reveal that the accuracy of estimation is higher when the hyperparameters are tuned using PSO.

Predictive analytics approaches for software effort estimation: A review

Background/Objective: In Software Effort Estimation (SEE), predicting the amount of time taken in human hours or months for software development is considered as a cumbersome process. SEE consists of both Software Development Effort Estimation (SDEE) and Software Maintenance Effort Estimation (SMEE). Over estimation or under estimation of software effort results in project cancellation or project failure. The objective of this study is to identify the best performing model for software Effort Estimation through experimental comparison with various Machine learning algorithms. Methods: Software Effort Estimation was addressed by using various machine learning techniques such as Multilinear Regression, Ridge Regression, Lasso Regression, ElasticNet Regression, Random Forest, Support Vector Machine, Decision Tree and NeuralNet to recognize best performing model. Datasets used are Desharnais, Maxwell, China and Albrecht datasets. Evaluation metrics considered are Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE) and R-Squared. Findings: Experiments on various machine learning algorithms for software Effort Estimation determines that Support Vector Machine produced the best performance comparatively with other algorithms. Keywords: Machine learning; software effort estimation; regression models; classification models

Fuzzy Optimization Algorithm for Software Cost Estimation

Estimation of a software cost depends on a probabilistic model and thus it doesn't create precise values. In any case, accessibility of good chronicled information combined with a efficient technique can create improved outcomes. This paper, we have displayed a Software Effort Estimation Model utilizing PSO and Fuzzy Logic. Fuzzy sets have been utilized for displaying uncertainty and imprecision in estimation of effort while PSO has been utilized for tuning parameters. This has been seen from the outcomes that Fuzzy-PSO intelligence gives precise outcomes when compared through its different partners. This system relies upon thinking by linguistic quantifiers and fuzzy logic. This kind of model holds well, when the product plans are communicated by absolute or potentially arithmetical data. Along these lines, this projected methodology improves the old style correlation process that doesn't think about clear cut data. In the fuzzy correlation model, fuzzy sets are used to describe both the clear cut and the arithmetical data.

Evolutionary neural network classifiers for software effort estimation

The estimation of software development efforts has become a crucial activity in software project management. Due to this importance, many researchers focused their efforts on proposing models for relationship construction between efforts and software size and requirements. However, there are still gaps and problems in software effort's estimation process; due to the lack of enough data available in the initial stage of project life cycle. The need for an enhanced and an accurate method for software effort estimation is an urgent issue that challenged software project-management researchers around the world. This work proposes a model based on artificial neural network (ANN) and dragonfly algorithm (DA), in order to provide more accurate model for software effort estimation. The applicability of the model was evaluated using several experiments and the results were in favour of the enhancement with more accurate effort estimation.

A systematic literature review of software effort prediction using machine learning methods

AbstractMachine learning (ML) techniques have been widely investigated for building prediction models, able to estimate software development effort as well as to improve the accuracy of other estimation techniques. The objective of this paper is to systematically review the recent studies which used and discussed the software effort estimation models built using ML techniques. The performed literature review is based on the empirical studies published in the time period of January 1991 to December 2017, by employing widely used guidelines. The review has selected a total of 75 primary studies after the careful filtering of inclusion/exclusion and quality assessment criteria. The performed analysis reveals that artificial neural network (ANN) as ML model, NASA as dataset, and mean magnitude of relative error (MMRE) as accuracy measure are widely used in the selected studies. ANN and support vector machine (SVM) are the two techniques which have outperformed other ML techniques in more studies. Regression techniques are the mostly used among the non‐ML techniques, which outperformed other ML techniques in about 19 studies. Moreover, SVM and regression techniques in combination are characterized by better predictions when compared with other ML and non‐ML techniques.

Uncertainty management in software effort estimation using a consistent fuzzy analogy-based method

Software effort estimation is a critical task in software project development management. Unfortunately, the uncertainty and inaccuracy are inherent properties of the software effort estimation environment. These are caused by the limited capabilities of the managers, to foresee, measure and describe factors influencing the software effort. The promising Fuzzy Analogy-based Software Effort Estimation model (FASEE) employs successfully fuzzy logic with approximate reasoning theory to handle imprecision and reasoning under uncertainty. Also, FASEE use possibility distribution to quantify the uncertainty in the estimate that aid the software managers to assess risks. Yet, the FASEE suffer from the low data quality and the uncertainty induced in the reasoning process. In this paper, we propose an enhancement of the FASEE, by imposing consistency criteria to deal with the aforementioned drawbacks. So, the underlying model, called Consistent Fuzzy Analogy-based Software Effort Estimation (C-FASEE) is endowed with two capabilities. The first one introduces consistency criteria in attribute representation by fuzzy sets to enable fitting each attribute to the software effort. The second one introduces a new relation of confidence to measure the extent that the resulted most similar projects respect the assumption “similar projects have similar efforts”. Moreover, the C-FASEE method provide a fuzzy estimate of the most possible fuzzy set will the true effort of the new software project falls in. This allow to the software manager to assess risks more optimally. The proposed C-FASEE is validated over thirteen software project datasets that represent different complexities. The obtained results are compared to variant methods of the analogy-based software effort estimation approach. The experimental results show that our proposal provides a good estimation accuracy of and has significantly best performance against the comparison methods.

Utility of CK Metrics in Predicting Size of Board-Based Software Games

Software size is one of the most important inputs of many software cost and effort estimation models. Early estimation of software plays an important role at the time of project inception. An accurate estimate of software size is, therefore, crucial for planning, managing, and controlling software development projects dealing with the development of software games. However, software size is unavailable during early phase of software development. This research determines the utility of CK (Chidamber and Kemerer) metrics, a well-known suite of object-oriented metrics, in estimating the size of software applications using the information from its UML (Unified Modeling Language) class diagram. This work focuses on a small subset dealing with board-based software games. Almost sixty games written using an object-oriented programming language are downloaded from open source repositories, analyzed and used to calibrate a regression-based size estimation model. Forward stepwise MLR (Multiple Linear Regression) is used for model fitting. The model thus obtained is assessed using a variety of accuracy measures such as MMRE (Mean Magnitude of Relative Error), Prediction of x(PRED(x)), MdMRE (Median of Relative Error) and validated using K-fold cross validation. The accuracy of this model is also compared with an existing model tailored for size estimation of board games. Based on a small subset of desktop games developed in various object-oriented languages, we obtained a model using CK metrics and forward stepwise multiple linear regression with reasonable estimation accuracy as indicated by the value of the coefficient of determination (R 2 = 0.756).Comparison results indicate that the existing size estimation model outperforms the model derived using CK metrics in terms of accuracy of prediction.

Duplex output software effort estimation model with self-guided interpretation

ContextSoftware effort estimation (SEE) plays a key role in predicting the effort needed to complete software development task. However, the conclusion instability across learners has affected the implementation of SEE models. This instability can be attributed to the lack of an effort classification benchmark that software researchers and practitioners can use to facilitate and interpret prediction results. ObjectiveTo ameliorate the conclusion instability challenge by introducing a classification and self-guided interpretation scheme for SEE. MethodWe first used the density quantile function to discretise the effort recorded in 14 datasets into three classes (high, low and moderate) and built regression models for these datasets. The results of the regression models were an effort estimate, termed output 1, which was then classified into an effort class, termed output 2. We refer to the models generated in this study as duplex output models as they return two outputs. The introduced duplex output models trained with the leave-one-out cross validation and evaluated with MAE, BMMRE and adjusted R2, can be used to predict both the software effort and the class of software effort estimate. Robust statistical tests (Welch's t-test and Kruskal-Wallis H-test) were used to examine the statistical significant differences in the models’ prediction performances. ResultsWe observed the following: (1) the duplex output models not only predicted the effort estimates, they also offered a guide to interpreting the effort expended; (2) incorporating the genetic search algorithm into the duplex output model allowed the sampling of relevant features for improved prediction accuracy; and (3) ElasticNet, a hybrid regression, provided superior prediction accuracy over the ATLM, the state-of-the-art baseline regression. ConclusionThe results show that the duplex output model provides a self-guided benchmark for interpreting estimated software effort. ElasticNet can also serve as a baseline model for SEE.

Multi-criteria decision making approach for the selection of software effort estimation model

Multi-criteria decision making approach for the selection of software effort estimation model