TUPUX: An Estimation Tool for Incremental Software Development Projects

Software developers need to cope with a massive amount of knowledge throughout the typical life cycle of modern projects. This knowledge includes expertise related to the software development phases (e.g., programming, testing) using a wide variety of methods and tools, including development methodologies (e.g., waterfall, agile), software tools (e.g., Eclipse), programming languages (e.g., Java, SQL), and deployment strategies (e.g., Docker, Jenkins). However, there is no explicit integration of these various types of knowledge with software development projects so that developers can avoid having to search over and over for similar and recurrent solutions to tasks and reuse this knowledge. Specifically, Q&A sites such as Stack Overflow are used by developers to share software development knowledge through posts published in several categories, but there is no link between these posts and the tasks developers perform. In this paper, we present an approach that (i) allows developers to associate project tasks with Stack Overflow posts, and (ii) recommends which Stack Overflow posts might be reused based on task similarity. We analyze an industry dataset, which contains project tasks associated with Stack Overflow posts, looking for the similarity of project tasks that reuse a Stack Overflow post. The approach indicates that when a software developer is performing a task, and this task is similar to another task that has been associated with a post, the same post can be recommended to the developer and possibly reused. We believe that this approach can significantly advance the state of the art of software knowledge reuse by supporting novel knowledge-project associations.

The increasing adoption of agile software development methodologies is amplifying the message that people are a first-order success factor of software projects. In this paper we describe a means to make certain people characteristics a first-class concern in software development projects. In particular, we present a systematic way of capturing, making explicit and effectively communicating human attitudes, beliefs and ways of thinking that influence individual and team work in software projects. Moreover, we provide an organised way to supplement and enhance software development methods, processes and project practice in order to explicitly take into account such human factors.

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 projects typically require effort estimation from the software development life cycle (SDLC) staff at the commencement of the software development activities. Inaccuracies in effort estimation of software projects early on in the SDLC continues to be a perennial problem. Typically, improved estimates for software projects are provided under circumstances where requirements engineering (RE) is completed. RE provides a key reference in the form of requirements for the estimation process. However, RE by itself may consume significant effort demanding the RE phase to be estimated as the first step in the SDLC. It is difficult to establish RE estimates, as RE usually has no tangible reference on which estimates can be based. This paper provides a practical estimation method for large and complex RE processes. We demonstrate using a case study snapshot in a large software organization that the method provides improved effort estimates for the RE process.

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Among critical stakeholders of software projects developed in public institutions are software developers and project managers affiliated to sub-contractor companies, who are working onsite at the public institutions. The aim of this study is to determine the effect of working onsite in public software projects for software developers and managers with respect to selected factors. The research is designed as an exploratory and qualitative study. The study participants were 10 software developers, one technical team leader and four project managers, all having long experiences in working on public software projects on client premises. Semi-structured interview technique is employed as the data collection method and “ analysis” was applied on the obtained participant responses. Following this analysis, “key ” approach was used to identify the issues to be further considered. It has been determined that the productivity of software developers working onsite at public institutions is low due to intense requests, shortened time to fulfill these requests, and long duration of authorization processes. Moreover, a finding of the study is that the quality of the developed projects and the motivation of the developers are considered to be low for the same aforementioned reasons. Among the foremost causes for these results are the fact that employees work under two different hierarchical structures one in the public institution and one in the software company, more than one managers assign work and there is an authority confusion between organizational units and people in the public institutions.

Software Requirements Changes is a typical phenomenon in any software development project. Restricting incoming changes might cause user dissatisfaction and allowing too many changes might cause delay in project delivery. Moreover, the acceptance or rejection of the change requests become challenging for software project managers when these changes are occurred in Software Development Phase. Where in Software Development Phase software artifacts are not in consistent state such as: some of the class artifacts are Fully Developed, some are Half Developed, some are Major Developed, some are Minor Developed and some are Not Developed yet. However, software effort estimation and change impact analysis are the two most common techniques which might help software project managers in accepting or rejecting change requests during Software Development Phase. The aim of this research is to develop a new software change effort estimation model which helps software project manager in estimating the effort for software Requirement Changes during Software Development Phase. Thus, this research has analyzed the existing effort estimation models and change impact analysis techniques for Softwrae Development Phase from the literature and proposed a new software change effort estimation model by combining change impact analysis technique with effort estimation model. Later, the new proposed model has been evaluated by selecting four small size software projects as case selections in applying experimental approch. The experiment results show that the overall Mean Magnitude Relative Error value produced by the new proposed model is under 25%. Hence it is concluded that the new proposed model is applicable in estimating the amount of effort for requirement changes during SDP.

<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.

The effort involved in developing a software product plays an important role in determining the success or failure. In the context of developing software using object oriented methodologies, traditional methods and metrics were extended to help managers in effort estimation activity. Software project managers require a reliable approach for effort estimation. It is especially important during the early stage of the software development life cycle. In this paper, the main goal is to estimate the cost of various software projects using class point approach and optimize the parameters using six types of adaptive regression techniques such as multi-layer perceptron, multivariate adaptive regression splines, projection pursuit regression, constrained topological mapping, K nearest neighbor regression and radial basis function network to achieve better accuracy. Also a comparative analysis of software effort estimation using these various adaptive regression techniques has been provided. By estimating the effort required to develop software projects accurately, we can have softwares with acceptable quality within budget and on planned schedules.

Software Cost Estimation is an important activity in software development projects. Various measurement methods are widely applied to estimate software cost, and one of them is Function Point Method. Function Point (FPA) method measures the software by measuring software functionality that provided to users based on design logic. The measurement consist of several steps: calculate Unadjusted Function Point (UFP), Value Adjustment Factor (VAF), Adjusted Function Point (AFP) and estimate the software cost. Software project that became the object of this research were four licensing projects from local government with total cost 229.680.000. The estimated total cost of four projects using Function Point method is 216.956.881 rupiahs. The percentage gap between the actual with the function point that is equal to 5.54%. It can be concluded that FPA is an accurate method and this cost estimation can be used by developers in determining the cost of software to be built

In this paper, we identify a set of factors that may be used to forecast software productivity and software development time. Software productivity was measured in function points per person hours, and software development time was measured in number of elapsed days. Using field data on over 130 field software projects from various industries, we empirically test the impact of team size, integrated computer aided software engineering (ICASE) tools, software development type, software development platform, and programming language type on the software development productivity and development time. Our results indicate that team size, software development type, software development platform, and programming language type significantly impact software development productivity. However, only team size significantly impacts software development time. Our results indicate that effective management of software development teams, and using different management strategies for different software development type environments may improve software development productivity.

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.

Software development effort estimation is one of the most crucial activities in software engineering. Effort estimation permits managers and software engineers to anticipate, forecast, and precisely quote the schedule, budget and manpower requirements. By accurately estimating the effort; software projects can be saved from under run or over run. In this paper we have summarized and then analyzed the past work of software effort estimation in a systematic way. 10 researches were surveyed and explained briefly that how they are contributing towards solving the effort estimation problem in terms of time, cost or test. It also emphasizes that various effort estimation models have different pros and cons and can be used in different context on basis of different types of historical data. The survey discovered the most popular models used for effort prediction are supervised learning algorithms. The trends identified through this survey can help in exploring the potential research areas.

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. >

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.