Measuring Software Size and Effort Estimation on Islamic Banking Application

Software cost estimation is a key open issue for the software industry, which suffers from cost overruns frequently. As the most popular technique for object-oriented software cost estimation, Use Case Points(UCP) method, however, has two major drawbacks: the uncertainty of the cost factors and the abrupt classification. To address these two issues, we propose the Extended Use Case Points (EUCP) method. With a probabilistic cost model constructed from integrating fuzzy set theory and Bayesian Belief Networks(BBNs) with the UCP method, EUCP provides a probability distribution of cost and a refined gradual classification, which mitigate the uncertainty of cost factors and improve the accuracy of classification. In this paper, we provides two case studies to demonstrate the effectiveness of EUCP in the real life.

There are many different ways to size estimate of software projects. However, software developers always have problems in choosing the right method for their projects. The Use Case Points (UCP) is among these common methods, but there is still some complexity in using them in a way that greatly contributes to obtaining accurate estimates. This paper presents an improvement of the UCP method, whereby an activity diagram is used and adapted and a weighting mechanism is developed. The improved method "Merging use case points (M-UCP)" will contribute to reducing the complexity of the traditional UCP method in the estimation process. In addition, a case study is introduced to test the refined method, and the results showed that the method performed better in comparison with previous methods in size estimating software development.

<i>Introduction</i>: The precise estimation of software effort is a significant difficulty that project managers encounter during software development. Inaccurate forecasting leads to either overestimating or underestimating software effort, which can be detrimental for stakeholders. The International Function Point Users Group&#x2019;s Function Point Analysis (FPA) method is one of the most critical methods for software effort estimation. However, the practice of using the FPA method in the same fashion across all software areas needs to be reexamined. <i>Aim</i>: We propose a model for evaluating the influence of data clustering on software development effort estimation and then finding the best clustering method. We call this model the effort estimation using machine learning applied to the clusters (EEAC) model. <i>Method</i>: We cluster the dataset according to the clustering method and then apply the FPA and EEAC methods to these clusters for effort estimation. The clustering methods we use in this study include five categorical variable criteria (Development Platform, Industrial Sector, Language Type, Organization Type, and Relative Size) and the k-means clustering algorithm. <i>Results</i>: The experimental results show that the estimation accuracy obtaining with clustering consistently outperforms the accuracy without clustering for both the FPA and EEAC methods. Significantly, using the FPA method, the average improvement rate from using clustering as opposed to non-clustered was highest at 58.06%, according to the RMSE. With the EEAC method, this number reached 65.53%. The Industry Sector categorical variable achieves the best accuracy estimation compared to the other clustering criteria and k-means clustering. The improvement in accuracy in terms of the RMSE when applying this criterion is 63.68% for the FPA method and 72.02% for the EEAC method. <i>Conclusion</i>: Better results are obtained through dataset clustering compared to no clustering for both the FPA and EEAC methods. The Industry Sector is the most suitable clustering method among the tested clustering methods.

Accurate software effort estimation is a persistent challenge in project management, as a significant share of project failures is attributed to inadequate prior risk consideration. Among the widely adopted methodologies that are used for early estimation of the success of a project is the Use Case Points (UCP) method, yet its framework does not take into consideration risk factors, undermining its reliability. To improve upon this limitation, this study introduces UCPRisk, a rule-based expert-guided UCP model that explicitly integrates risk factors across six dimensions: requirements, planning and control, project complexity, user involvement, team dynamics, and organizational environment. The model was evaluated through three public service application case studies, producing effort estimates of 1,564.42 man-hours (agriculture application), 7,287.91 man-hours (company registration), and 4,669.17 man-hours (industrial permit registration). Compared to traditional UCP, UCPRisk reduced the average Relative Error (RE) to 24.3%, demonstrating improved alignment with actual project outcomes. While not a definitive cost calculation tool, UCPRisk provides a structured framework that embeds risk into early estimation, offering more realistic projections for software development.

Employee Application (EA) is a system information for realizing the latest personal data and integrated, provide accurate employee information for planning, development, welfare and control of employees. EA in STT-PLN began to be published since 2013. The software size of the EA STT-PLN will be measured with use case point method. Measurement of the software size of EA STT-PLN will be measured with Use Case Point upon use case diagram for EA STT-PLN as shown in the project has small software size where score Use Case Point (UCP) = 70.34. UCP is another alternative implementation method which can be applied to measure application software size whenever needed to deal with time, money and people.

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

The cost of error correction has been increasing exponentially with the advancement of software industry. To minimize software errors, it is necessary to extract accurate requirements in the early stage of software development. In the previous study, we extracted the priorities of requirements based on the Use Case Point (UCP), which however revealed the issues inherent to the existing UCP as follows. (i) The UCP failed to specify the structure of use cases or the method of write the use cases, and (ii) the number of transactions determined the use case weight in the UCP. Yet, efforts taken for implementation depend on the types and number of operations performed in each transaction. To address these issues, the present paper proposes an improved UCP and applies it to the prioritization. The proposed method enables more accurate measurement than the existing UCP-based prioritization.

Software effort and cost estimation is a very important activity that includes very uncertain elements. In the context of object oriented software, traditional methods and metrics were extended to help managers in this activity. The metric use case points (UCP) is an example of metric that can be used. UCP considers functional aspects of the use case (UC) model, widely used in most organizations in the early phases of the development. However, the metric UCP presents some limitations mainly related to the granularity of the UC. To overcome these limitations, this paper introduces two metrics, also based on UCs. The first one, named USP (use case size points), considers the internal structures of the UC and better captures its functionality. The second one, named FUSP (fuzzy use case size points), considers concepts of the fuzzy set theory to create gradual classifications that better deal with uncertainty. Results from an empirical evaluation show the applicability and some advantages of the proposed metrics

Estimating the software size and effort helps in early prediction of uncertainties in software development. Determining the size helps in ascertaining the effort, cost, and schedule for entire project. In context of mobile software, the existing techniques for software effort estimations could be adapted as such or with modifications. There are many software size and effort estimation metrics. In this paper, Use Case Point (UCP) metric is aimed for estimating size and effort for mobile application. Five android mobile applications are considered as a case study, and difference in actual effort and estimated effort is evaluated. Modified UCP has been also proposed to improve the results by considering mobile-specific characteristics.

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.

Software cost estimation is crucial to software management, which has received considerable attention from both industry and academia. Software size is an important metric that forms the cornerstone of software cost estimation. The function point has been proven to be a useful software size unit for size estimation and has been successfully implemented in many countries. However, in current practice, the rule of function point size method is complicated and performed manually. Consequently, it is costly in both time and resources spent to apply these methods, especially in the scenario of large-scale software development in the industry. In this paper, a deep learning-based named entity recognition (NER) model was designed in place of manual function point recognition. In particular, a BiLSTM-CRF model was trained on previously labeled requirements in the industry to classify the function point type of new requirements in the same domain. The proposed method was verified on 29 real projects provided by our industry partner. A comparative experiment was designed for the quantitative evaluation of efficiency improvement of the proposed NER model aided function point estimation. The result suggests that, for the NER model, the precision and F1 of the BiLSTM-CRF-based function point analysis on test samples achieved 94.5% and 80.3%, respectively. Moreover, the improvement in the efficiency of the software size estimation process achieved an average of 38.6%, which is a significant enhancement for the function point-based software size estimation.

Software effort estimation is crucial task in software development life cycle. There are many model exists for estimating the software project effort like regression model, expert judgment, algorithmic and non-algorithmic. But, still software industries are lacking to have more accurate effort estimation method due to which they will lead to failure. In this work, an enhanced Use Case Point model (Algorithmic model) is introduced to overcome this problem which is based on the existing Use Case Point (UCP) model. A case study is also performed for comparative analysis by taking five software projects into consideration and different evaluation criteria like MMRE, MMER, MSE, RMSE and PRED(x) are used to estimate the accuracy of the proposed model.

Effective management of the software development process requires that management be able to estimate total development effort and cost. One of the fundamental problems associated with effort and cost estimation is the a priori estimation of software size. Function point analysis has emerged over the last decade as a popular tool for this task. Criticisms of the method that relate to the way in which function counts are calculated and the impact of the processing complexity adjustment on the function point count have arisen. SPQR/20 function points among others are claimed to overcome some of these criticisms. The SPQR/20 function point method is compared to traditional function point analysis as a measure of software size in an empirical study of MIS environments. In a study of 64 projects in one organization it was found that both methods would appear equally satisfactory. However consistent use of one method should occur since the individual counts differ considerably. >

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. >

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.