Toward Improving the Efficiency of Software Development Effort Estimation via Clustering Analysis

In this study, we extend the study of Function Point Analysis (FPA) for the measurement of source code based migration project using the migration tooling options. FPA is an objective and structured technique to measure software size by quantifying its functionality provided to the user, based on the requirements and logical design. It is noticed that the procedure for the above measurement is not fully described in FPA method. Hence, the same estimation concept has been applied to an in-house project on an experimental basis at a leading software development organization. The yielded results are compared with FPA for the re-engineering project.

To maintain customer trust, Islamic banking continues to improve its services which rely on the use of information technology (software and its supporting peripherals). In the meantime, research on software in Islamic banking are still finite. Thus, the authors are interested in calculating the size and effort of working on the core application of an Islamic bank, which is called the Islamic Banking Application (IBA). Two methods are used in the study to see the size of the IBA. The size can be seen from the point of view of functional-based processes and non-functional aspects. The functional one, we propose the Use Case Points (UCP) method. In the UCP method, there are at least4 steps that must be passed for measuring software size. On the other hand, for non-functional measurement, we used the original Function Points Analysis (FPA) concepts. As a result, this research produces size measurement software using the FPA technique of 2,109.40 function points, while the UCP method results in 3,024.54 use case points. To convert size measurement into effort estimation, the author uses previous research with a productivity rate of 8.2 person/hour. Thus, the predicted effort consumed to develop the IBA is 17,297.08 person/hour (based on FPA technique) and 24, S01.23 person/hour (by UCP method). We found the deviation of 7,504 person/hour between both methods. This is due to the existence of different measurement parameters.

Function point analysis is a widely used metric in the software industry for development effort estimation. It was proposed in the 1970s, and then standardized by the International Function Point Users Group, as accepted by many organizations worldwide. While the software industry has grown rapidly, the weight values specified for the standard function point counting have remained the same since its inception. Another problem is that software development in different industry sectors is peculiar, but basic rules apply to all. These raise important questions about the validity of weight values in practical applications. In this study, we propose an algorithm for calibrating the standardized functional complexity weights, aiming to estimate a more accurate software size that fits specific software applications, reflects software industry trends, and improves the effort estimation of software projects. The results show that the proposed algorithms improve effort estimation accuracy against the baseline method.

Some developer very capable of developing good quality working applications, but they don’t have the right value and price for their products, and it is because they did not use the right method to calculate the size of the product and valuate them correctly, most of them use analogy and expert judgment to perform their product sizing and valuation. This situation will create inconsistency in doing the pricing and it will create disadvantages for both developers and users. Function point analysis provides a methodology to perform software size calculation using algorithmic approaches that gives comprehensive and consistent results. In this document, I will explain step by step the processes of calculating the Function point using FPA procedures by implementing calculation formula of EI, EO, EQ, ILF, EIF, and VAP. I also will implement them to calculate the size of the “XIbar” application system, especially module customer care and customer info. By doing all these activities I hope will provide simple templates and procedures for calculating the size of application software that can be used easily by developers, students, and others who need it.

Over the past ten couple of years, there is a variety of effort models proposed by academicians and practitioners at early stage of software development life cycle. Some addressed that efforts could be predicted using Lines of Codes(LOC) and COCOMO, others emphasized that it could be made using Function Point Analysis(FPA) or others. The study seeks to develop a model that estimates software effort by studying and analyzing small and medium scale application software. To develop such a model, 50 completed software projects are collected from a software company. With the sample data, design team factors are identified and extracted. By applying them to simple regression analyses, a prediction of software of effort estimates with accuracy of MMRE = 9% was constructed. The results give several benefits. First, the estimation problems are minimized due to the simple procedure used in identifying those factors. Second, the predicted software projects are only limited to a specific environment rather than being based upon industry environment. We believe the accuracy of effort estimates can be improved. According to the results analyzed, the work shows that it is possible to build up simple and useful prediction model based on data extracted at the early stage of software development life cycle. We hope this model can provide valuable ideas and suggestions for project designers for planning and controlling software projects in near future.

Software effort estimation is a crucial phase in software project management. Accuracy of estimation directly affects project success or failure. Managers try to estimate proper effort resources and this is a challenging issue for management. Having a set of tools and methodologies, estimation process can be made better. COCOMO is one of the most used model which has a parametric form. Also, artificial neural networks (ANN) are combined with COCOMO and these methods increased overall performance. However, effort estimation process generally produces one output; estimation value. It is a well-known issue that a project manager must keep in the mind that any estimation must have some upper and lower limits, boundaries. In this paper, a novel method, combining COCOMO used ANN with K-Means is used to estimate effort and possible boundaries. ANN output is used as input to K-Means sets and proper set value is calculated, including possible lower and upper effort estimation value. Experimental results are shown that proposed method has acceptable results over ANN and COCOMO.

It is pointed out that function point analysis has become well-established as a method for estimating software size and, sometimes, software effort. However, the accuracy of estimation models using function points to estimate size or effort has not been ascertained significantly. The authors summarize a study performed to assess the expected accuracy of three function point-based models: the Tecelote Software Program Acquisition Network Simulation (SPANS) model, the Checkpoint model, and the Costar model. It is shown that the three function point models can be useful in effort estimation, especially if they can be adjusted for bias. Based on the percentage tests, the unadjusted Checkpoint model was more accurate than the other two models. However, when models were adjusted for bias, the results were very similar. >

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.

IFPUG Function Points to COSMIC Function Points convertibility: A fine-grained statistical approach

Accurate software effort estimation is one of the key factors to a successful project by making a better software project plan. To improve the estimation accuracy of software effort, many studies usually aimed at proposing novel effort estimation methods or combining several approaches of the existing effort estimation methods. However, those researches did not consider the distribution of historical software project data which is an important part impacting to the effort estimation accuracy. In this paper, to improve effort estimation accuracy by least squares regression, we propose a data partitioning method by the accuracy measures, MRE and MER which are usually used to measure the effort estimation accuracy. Furthermore, the empirical experimentations are performed by using two industry data sets (the ISBSG Release 9 and the Bank data set which consists of the project data performed in a bank in Korea).

Nowadays, software effort estimation plays an important role in software project management due to its extensive use in industry to monitor progress, and performance, determine overall productivity and assist in project planning. After the success of methods such as IFPUG Function Point Analysis, MarkII Function Point Analysis, and COSMIC Full Function Points, several other extension methods have been introduced to be adopted in software projects. Despite the efficiency in measuring the software cost, software effort estimation, unfortunately, is facing several issues; it requires some knowledge, effort, and a significant amount of time to conduct the measurement, thus slightly ruining the advantages of this approach. This paper demonstrates a functional size measurement tool, named UML Point tool, that utilizes the concept of IFPUG Function Point Analysis directly to Unified Modeling Language (UML) model. The tool allows the UML eXchange Format (UXF) file to decode the UML model of mobile game requirement and extract the diagrams into component complexity, object interface complexity, and sequence diagram complexity, according to the defined measurement rules. UML Point tool then automatically compute the functional size, effort, time, human resources, and total development cost of mobile game. Besides, this paper also provides a simple case study to validate the tool. The initial results proved that the tool could be useful to improve estimation accuracy for mobile game application development and found to be reliable to be applied in the mobile game industry.

A critical factor in successful project management and effective planning is the analysis of software development complexity and effort. The use of industry-standard methods, such as Function Point Analysis, serves as an effective means of improving estimation accuracy while simultaneously reducing the cost of the estimation process itself. However, standard methods, most of which were developed several decades ago, are not sufficiently adapted to modern realities, including agile software development and the use of pre-built or standardized solutions. This study proposes knowledge representation model that combine production and frame-based approaches to address the challenge of assessing the complexity and effort of software development involving SaaS (Software as a Service) and PaaS (Platform as a Service) solutions. A knowledge base model has been developed, integrating both frame-based and production models while ensuring compatibility with Function Point Analysis. The developed models and their interactions can serve as the foundation for a decision-making model within an information technology framework for software development complexity assessment, considering environmental factors and evaluation criteria. The application of the proposed models will enable the creation of automated algorithms for software development complexity estimation under conditions of incomplete functional requirements. The proposed approach improves effort estimation by considering real-world implementation contexts, which is particularly relevant for modern IT projects. Additionally, the combination of frame-based and production models lays the groundwork for further integration with artificial intelligence and machine learning to automate effort estimation processes. Future research should focus on refining the decision-making model, conducting experimental validation against traditional methods, and expanding its capabilities using fuzzy logic and neural networks for dynamic complexity assessment. The integration of this approach into decision support systems (DSS) for software project and resource management also remains a promising direction.

This study proposes a Function Point Analysis (FPA) based software development effort estimation methodology integrated with Use Case Diagrams. These methods include identifying actor activities, classifying those activities into FPA categories, and calculating Unadjusted Function Points (UFP). Followed by the calculation of Technical Complexity Factors (TCF) and Adjusted Function Points (AFP), this study aims to produce more accurate man-hours estimates. Results show a UFP of 162 TCF of 11, AFP of 123.12, and an estimated effort of 1846.8 hours worked, while the actual effort is 1228 hours. Evaluation of estimates using the metrics Mean Magnitude of Relative Error (MMER) 0.34, Mean Magnitude of Relative Error (MMRE) 0.50, Mean Absolute Error (MAE) 618.80, Mean Balanced Relative Error (MBRE) 0.50, Mean Inverse Balanced Relative Error (MIBRE) 0.34, and Root Mean Squared Error (RMSE) 618.80, showed sufficient precision despite the overestimation. The study suggests the need for adjustments in TCF calculations and considering development environment factors in more detail to improve estimation accuracy. These findings are essential in improving the precision of effort estimation methodologies in software development, particularly in projects that use Use Case Diagrams as the primary framework.

The methods for Software Effort Estimation are divided into two, these methods are grouped into Non Machine Learning (non-ML) and Machine Learning (ML) methods [1]. The k-NN method has the disadvantage of being unable to tolerate irrelevant features and greatly affect the accuracy of k-NN. The k-NN method is also difficult to deal with missing data problems and feature categorization problems such as features that are not relevant, weight features that are not optimal, and the same features [2]. Whereas the dataset of Software Effort Estimation still has some serious challenges such as the characteristics of the data set, which are irrelevant features and the level of influence of each feature in the estimated data of the software effort [3]. This study compared the k-NN individual method with the combination of feature selection method with k-NN to find out which method was the best. The results showed that the Forward Selection (FS) method and Median Weighted Information Gain with k-Nearest Neighbor can overcome the problem of irrelevant features so as to increase accuracy in the RMSE Software Effort Estimation dataset, which is smaller in the Albrecht dataset of 5,953 using the Median method -WIG k-NN, the Miyazaki dataset is 55,421 and Kemerer is 123,081 using the FS k-NN method. The combination of kNN with Feature Selection is proven to be able to improve the estimation results better than kNN individuals. With the FS k-NN method being the best by winning in 2 datasets Miyazaki and Kemerer.

Analogy‐based effort estimation (ABE) is one of the efficient methods for software effort estimation because of its outstanding performance and capability of handling noisy datasets. Conventional ABE models usually use the same number of analogies for all projects in the datasets in order to make good estimates. The authors’ claim is that using same number of analogies may produce overall best performance for the whole dataset but not necessarily best performance for each individual project. Therefore there is a need to better understand the dataset characteristics in order to discover the optimum set of analogies for each project rather than using a static k nearest projects. The authors propose a new technique based on bisecting k‐ medoids clustering algorithm to come up with the best set of analogies for each individual project before making the prediction. With bisecting k‐ medoids it is possible to better understand the dataset characteristic, and automatically find best set of analogies for each test project. Performance figures of the proposed estimation method are promising and better than those of other regular ABE models.