Estimating Software Effort Using an ANN Model Based on Use Case Points

In this paper, we propose a novel Artificial Neural Network (ANN) to predict software effort from use case diagrams based on the Use Case Point (UCP) model. The inputs of this model are software size, productivity and complexity, while the output is the predicted software effort. A multiple linear regression model with three independent variables (same inputs of the ANN) and one dependent variable (effort) is also introduced. Our data repository contains 240 data points in which, 214 are industrial and 26 are educational projects. Both the regression and ANN models were trained using 168 data points and tested using 72 data points. The ANN model was evaluated using the MMER and PRED criteria against the regression model, as well as the UCP model that estimates effort from use cases. Results show that the ANN model is a competitive model with respect to other regression models and can be used as an alternative to predict software effort based on the UCP method

Optimizing complexity weight parameter of use case points estimation using particle swarm optimization

Among algorithmic-based frameworks for software development effort estimation, Use Case Points I s one of the most used. Use Case Points is a well-known estimation framework designed mainly for object-oriented projects. Use Case Points uses the use case complexity weight as its essential parameter. The parameter is calculated with the number of actors and transactions of the use case. Nevertheless, use case complexity weight is discontinuous, which can sometimes result in inaccurate measurements and abrupt classification of the use case. The objective of this work is to investigate the potential of integrating particle swarm optimization (PSO) with the Use Case Points framework. The optimizer algorithm is utilized to optimize the modified use case complexity weight parameter. We designed and conducted an experiment based on real-life data set from three software houses. The proposed model’s accuracy and performance evaluation metric is compared with other published results, which are standardized accuracy, effect size, mean balanced residual error, mean inverted balanced residual error, and mean absolute error. Moreover, the existing models as the benchmark are polynomial regression, multiple linear regression, weighted case-based reasoning with (PSO), fuzzy use case points, and standard Use Case Points. Experimental results show that the proposed model generates the best value of standardized accuracy of 99.27% and an effect size of 1.15 over the benchmark models. The results of our study are promising for researchers and practitioners because the proposed model is actually estimating, not guessing, and generating meaningful estimation with statistically and practically significant

Using actors and use cases for software size estimation

Software size estimation represents a complex task, which is based on data analysis or on an algorithmic estimation approach. Software size estimation is a nontrivial task, which is important for software project planning and management. In this paper, a new method called Actors and Use Cases Size Estimation is proposed. The new method is based on the number of actors and use cases only. The method is based on stepwise regression and led to a very significant reduction in errors when estimating the size of software systems compared to Use Case Points-based meth-ods. The proposed method is independent of Use Case Points, which allows the elimination of the effect of the inaccurate determination of Use Case Points components, because such components are not used in the proposed method. © 2021 by the author. Licensee MDPI, Basel, Switzerland.Faculty of Applied Informatics, Tomas Bata University in Zli

Estimering av Webutviklingsprosjekter : Use case poeng sammenliknet med eksperters estimater, funksjonspoeng, COCOMO II og WEBMO

This document is my master thesis at the Institute of Informatics, University of Oslo. This is a multiple case study where the focus is estimating web based software projects. For consultants in the software industry, the bidding process is one of the most important processes. At this point the costumer chooses which supplier they want to develop the planned software. Two important parts of the suppliers offer are price and time. The supplier has to make an estimate as good and realistic as possible to become the one that is chosen. When the costumer is going to choose its supplier, the estimates are studied in detail and the objective of the customer is to receive the best system as fast and cheap as possible. This may lead to incorrect estimates both when time and money are concerned because of the competition between suppliers. It is useful to study a model of estimation, to help the customer understand the price and development time of the planned software. It is also useful to discuss which estimation approach that a costumer understands. Estimates are important when starting a software project. A costumer needs to know time and costs to see if they can afford the software. For suppliers estimates are important to calculate income and how long that project will take. This way they will also know when the developers will be ready to start new projects. Estimates are often based on the experiences of the experts. One challenge with these estimates are that they have to be done early, even before the suppliers know the details about the system. It is therefore important to know how to estimate when only the functionality described in the user specification is known. It is useful to study how the user specification, specially the functional requirements, can be used when estimating. In May 2003 the Software Engineering group at Simula Research Laboratory began a research on the development of web based software. Four suppliers developed a research database, called “The Simula Database of Empirical Studies”, simultaneously as the researchers was doing a research on the development. This thesis looks at the estimation process in the development. The main focus is to study how an estimation approach, based on use case, may support both costumers and suppliers in the bidding process. The use case approach is based on a systems functional requirement written in use cases. There is a huge interest in the approach, but it is still necessary to adjust it to different kinds of projects. This thesis studies how an approach for use case based estimation may be used to estimate web based software by testing it on the projects and compare it to other more established methods. The chosen methods are Function Points, COCOMO II early design and WEBMO. The most important aspects discussed are; information requirements, how the approaches estimates compared to how experts estimates, actual effort, which quality the approaches plan and also when the approaches can be used and who to use them. This study shows that the size measure in the use case point approach is easier to use for people without any technical knowledge, for instance a costumer in software projects, than function point. This predicts well structured use cases. This study suggests guidelines for use case structuring, when the use cases are very detailed from the beginning. Further, the study shows that cost drivers bound to quality of code like maintenance, reuse, documentation, complexity and experience affects the effort to a greater extent. The study looks at how such cost drivers can be used in web projects. In this case the estimation approaches estimated as good as, or better than the experts. This indicates that the use case point approach may support both costumer and developer in a bidding process where a use case model describes the functional requirements

A systematic review of software development cost estimation studies

This paper aims to provide a basis for the improvement of software estimation research through a systematic review of previous work. The review identifies 304 software cost estimation papers in 76 journals and classifies the papers according to research topic, estimation approach, research approach, study context and data set. A web-based library of these cost estimation papers is provided to ease the identification of relevant estimation research results. The review results combined with other knowledge provide support for recommendations for future software cost estimation research, including: 1) Increase the breadth of the search for relevant studies, 2) Search manually for relevant papers within a carefully selected set of journals when completeness is essential, 3) Conduct more studies on estimation methods commonly used by the software industry, and, 4) Increase the awareness of how properties of the data sets impact the results when evaluating estimation methods

User Story Software Estimation:a Simplification of Software Estimation Model with Distributed Extreme Programming Estimation Technique

Software estimation is an area of software engineering concerned with the identification, classification and measurement of features of software that affect the cost of developing and sustaining computer programs [19]. Measuring the software through software estimation has purpose to know the complexity of the software, estimate the human resources, and get better visibility of execution and process model. There is a lot of software estimation that work sufficiently in certain conditions or step in software engineering for example measuring line of codes, function point, COCOMO, or use case points. This paper proposes another estimation technique called Distributed eXtreme Programming Estimation (DXP Estimation). DXP estimation provides a basic technique for the team that using eXtreme Programming method in onsite or distributed development. According to writer knowledge this is a first estimation technique that applied into agile method in eXtreme Programming

Estimating software project effort using analogies

Accurate project effort prediction is an important goal for the software engineering community. To date most work has focused upon building algorithmic models of effort, for example COCOMO. These can be calibrated to local environments. We describe an alternative approach to estimation based upon the use of analogies. The underlying principle is to characterise projects in terms of features (for example, the number of interfaces, the development method or the size of the functional requirements document). Completed projects are stored and then the problem becomes one of finding the most similar projects to the one for which a prediction is required. Similarity is defined as Euclidean distance in n-dimensional space where n is the number of project features. Each dimension is standardised so all dimensions have equal weight. The known effort values of the nearest neighbours to the new project are then used as the basis for the prediction. The process is automated using a PC based tool known as ANGEL. The method is validated on nine different industrial datasets (a total of 275 projects) and in all cases analogy outperforms algorithmic models based upon stepwise regression. From this work we argue that estimation by analogy is a viable technique that, at the very least, can be used by project managers to complement current estimation techniques

Estimating, planning and managing Agile Web development projects under a value-based perspective

Context: The processes of estimating, planning and managing are crucial for software development projects, since the results must be related to several business strategies. The broad expansion of the Internet and the global and interconnected economy make Web development projects be often characterized by expressions like delivering as soon as possible, reducing time to market and adapting to undefined requirements. In this kind of environment, traditional methodologies based on predictive techniques sometimes do not offer very satisfactory results. The rise of Agile methodologies and practices has provided some useful tools that, combined with Web Engineering techniques, can help to establish a framework to estimate, manage and plan Web development projects. Objective: This paper presents a proposal for estimating, planning and managing Web projects, by combining some existing Agile techniques with Web Engineering principles, presenting them as an unified framework which uses the business value to guide the delivery of features. Method: The proposal is analyzed by means of a case study, including a real-life project, in order to obtain relevant conclusions. Results: The results achieved after using the framework in a development project are presented, including interesting results on project planning and estimation, as well as on team productivity throughout the project. Conclusion: It is concluded that the framework can be useful in order to better manage Web-based projects, through a continuous value-based estimation and management process.Ministerio de Economía y Competitividad TIN2013-46928-C3-3-

mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data

We present the R-package mgm for the estimation of k-order Mixed Graphical Models (MGMs) and mixed Vector Autoregressive (mVAR) models in high-dimensional data. These are a useful extensions of graphical models for only one variable type, since data sets consisting of mixed types of variables (continuous, count, categorical) are ubiquitous. In addition, we allow to relax the stationarity assumption of both models by introducing time-varying versions MGMs and mVAR models based on a kernel weighting approach. Time-varying models offer a rich description of temporally evolving systems and allow to identify external influences on the model structure such as the impact of interventions. We provide the background of all implemented methods and provide fully reproducible examples that illustrate how to use the package

Predicting with sparse data

It is well known that effective prediction of project cost related factors is an important aspect of software engineering. Unfortunately, despite extensive research over more than 30 years, this remains a significant problem for many practitioners. A major obstacle is the absence of reliable and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper we describe our sparse data method (SDM) based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process (AHP). Our minimum data requirement is a single known point. The technique is supported by a software tool known as DataSalvage. We show, for data from two companies, how our approach — based upon expert judgement — adds value to expert judgement by producing significantly more accurate and less biased results. A sensitivity analysis shows that our approach is robust to pairwise comparison errors. We then describe the results of a small usability trial with a practising project manager. From this empirical work we conclude that the technique is promising and may help overcome some of the present barriers to effective project prediction