Software Effort Estimation using Machine Learning Technique

Predicting Software Effort Estimation Using Machine Learning Techniques, 2018

In software engineering, estimation plays a vital role in software development. Thus, affecting its cost and required effort and consequently influencing the overall success of software development. The error margin in Expert-Based, Analogy-Based and algorithmic based methods including: COCOMO, Function Point Analysis and Use-Case-Points is quite significant, which exposes software projects to the danger of delays and running over-budget. To obtain better estimation, we propose an alternative method through performing data mining on historical data. This paper suggests performing this prediction using three machine learning techniques that were applied to a preprocessed COCOMO NASA benchmark data which covered 93 projects: Naïve Bayes, Logistic Regression and Random Forests. The generated models were tested using five folds cross-validation and were evaluated using Classification Accuracy, Precision, Recall, and AUC. The estimation results were then compared to COCOMO estimation. All the applied techniques were successful in achieving better results than the compared COCOMO model. However, the best performance was obtained using both Naïve Bayes and Random Forests. Despite the fact that Naïve Bayes outperformed both of the other two techniques in its ROC curve and Recall score, Random Forests has a better Confusion Matrix and scored better in both Classification Accuracy, and Precision measures. The results of this work confirm the validity of data mining in general and the applied technique in particular for software estimation.

International Journal of Advanced Computer Science and Applications, 2011

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.

2017

Accurate Software Effort Estimation is vital to the areas of Software Project Management. It is a process to predict the Effort in terms of cost and time, required to develop a software product. Traditionally, researchers have used the off the shelf empirical models like COCOMO or developed various methods using statistical approaches like regression and analogy based methods but these methods exhibit a number of shortfalls. To predict the effort at early stages is really difficult as very less information is available. To improve the effort estimation accuracy, an alternative is to use machine learning (ML) techniques and many researchers have proposed plethora of such machine learning based models. This paper aims to systematically analyze various machine learning models considering the traits like type of machine learning method used, estimation accuracy gained with that method, dataset used and its comparison with empirical model. Although researchers have started exploring Mach...

IEEE access, 2024

The average software company spends a huge amount of its revenue on R&D for how to deliver software on time. Accurate software effort estimation is critical for successful project planning, resource allocation, and on-time delivery within budget for sustainable software development. However, both overestimation and underestimation pose significant challenges in software development, necessitating continuous improvement in estimation techniques. This study reviews recent machine learning approaches exploited to enhance software effort estimation (SEE) accuracy, focusing on research published between 2020 and 2023. The literature review employed an approach to identify pertinent research on machine learning techniques for software estimation efforts. Additionally, comparative experiments were conducted employing five commonly used ML methods: K-Nearest Neighbor, Support Vector Machine, Random Forest, Logistic Regression, and LASSO Regression. These techniques were assessed using five widely employed accuracy metrics such as Mean Squared Error (MSE), Mean Magnitude of Relative Error (MMRE), R-squared, Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) on seven benchmark datasets (Albrecht, Desharnais, China, Kemerer, Mayazaki94, Maxwell, COCOMO). By carefully reviewing study quality, analyzing results across the literature, and rigorously evaluating experimental outcomes, clear conclusions were drawn about the most promising techniques for achieving state-of-the-art accuracy in estimating software effort. This study makes three key contributions to the field: firstly, it furnishes a thorough overview of recent machine learning research in software effort estimation (SEE); secondly, it provides data-driven guidance for researchers and practitioners to select optimal methods for accurate effort estimation; and thirdly, it demonstrates the performance of publicly available datasets through experimental analysis. Enhanced estimation supports the development of better predictive models for software project time, cost, and staffing needs. The findings aim to focus future research directions and tool development toward the most accurate machine learning approaches for modeling software development effort, costs, and delivery schedules.

Effort Estimation (EE) is a technique for finding the entire effort required to predict the accuracy of a model. It's a significant chore in software application development practice. To find accurate estimation, numerous predictive models have developed in recent times. The estimate prepared during the early stage of a model expansion is inaccurate since requirements at that time are not very clear, but as the model progresses, the accuracy of the estimation increases. Therefore, accurate estimation is essential to choose for each software application model development. Here, Linear Regression (LR), Multi-layer perceptron (MLP), Random Forest (RF) algorithms are implemented using WEKA toolkit, and results shows that Linear Regression shows better estimation accuracy than Multilayer Perceptron and Random Forest.

IOP Conference Series: Materials Science and Engineering, 2019

Nowadays the significant trend of the effort estimation is in demand. It needs more data to be collected and the stakeholders require an effective and efficient software for processing, which makes the hardware and software cost development becomes steeply increasing. This scenario is true especially in the area of large industry, as the size of a software project is becoming more complex and bigger, the complexity of estimation is continuously increased. Effort estimation is part of the software engineering economic study on how to manage limited resources in a way a project could meet its target goal in a specified schedule, budget and scope. It is necessary to develop or adopt a useful software development process in executing a software development project by acting as a key constraint to the project. The accuracy of estimation is the main critical evaluation for every study. Recently, the machine learning techniques are becoming widely used in many effort estimation problems bu...

IEEE ACCESS, 2024

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.

2010 3rd International Conference on Emerging Trends in Engineering and Technology, 2010

Machine Learning deals with the issue of how to build programs that improve their performance at some task through experience. This paper deals with the subject of applying machine learning methods to software engineering .For effort estimation which not only provide an estimation but also confidence interval for it. The robust confidence intervals do not depend on the form of probability distribution of the errors in the training set. This paper compares various regression methods for software effort estimation with the help of number of experiments performed using NASA datasets [18] and to show that robust confidence intervals can be successfully built.

2013 Third International Conference on Communications and Information Technology (ICCIT), 2013

Accurate software effort estimation has been a challenge for many software practitioners and project managers. Underestimation leads to disruption in the project's estimated cost and delivery. On the other hand, overestimation causes outbidding and financial losses in business. Many software estimation models exist; however, none have been proven to be the best in all situations. In this paper, a decision tree forest (DTF) model is compared to a traditional decision tree (DT) model, as well as a multiple linear regression model (MLR). The evaluation was conducted using ISBSG and Desharnais industrial datasets. Results show that the DTF model is competitive and can be used as an alternative in software effort prediction.

ACM SIGSOFT Software Engineering Notes, 2010

Software effort estimation is an important area in the field of software engineering. If the software development effort is over estimated it may lead to tight time schedules and thus quality and testing of software may be compromised. In contrast, if the software development effort is underestimated it may lead to over allocation of man power and resource. There are many models proposed in the literature for estimating software effort. In this paper, we analyze machine learning methods in order to develop models to predict software development effort we used Maxwell data consisting 63 projects. The results show that linear regression, MSP and M5Rules are effective methods for predicting software development effort.