SEERA: a software cost estimation dataset for constrained environments

Software cost and time estimation is the process of estimating the cost and time required to develop a software system. Software cost and time estimation supports the planning and tracking of software projects. Effectively controlling the expensive investment of software development is one of the important issues in software project management. Estimating software development cost with high precision is still a great challenge for project managers, because it allows for considerable financial and strategic planning. Software cost estimation refers to the predictions of the likely amount of effort, time, and staffing levels required to build a software system. A very helpful form of cost estimation is the one made at an early stage during a project, when the costing of the project is proposed for approval. However, estimates at the early stages of the development are the most difficult to obtain. In this paper a novel Constructive Cost Model (COCOMO) based on soft computing approach is proposed for software cost estimation. This model carries some of the desirable features of neural networks approach, such as learning ability and good interpretability, while maintaining the merits of the COCOMO model. Unlike the standard neural networks approach, the proposed model can be interpreted and validated by experts, and has good generalisation capability. The model deals effectively with imprecise and uncertain input and enhances the reliability of software cost estimates. From the experimental results, it was concluded that, by the proposed neural network model, the accuracy of cost estimation can be improved and the estimated cost can be very close to the actual cost.

Due to the complexity of Service-Oriented Architecture (SOA), cost and effort estimation for SOA-based software development is more difficult than that for traditional software development. Unfortunately, there is a lack of published work about cost and effort estimation for SOA-based software. Existing cost estimation approaches are inadequate to address the complex service-oriented systems. This paper proposes a novel framework based on Divide-and-Conquer (D&C) for cost estimation for building SOA-based software. By dealing with separately development parts, the D&C framework can help organizations simplify and regulate SOA implementation cost estimation. Furthermore, both cost estimation modeling and software sizing work can be satisfied respectively by switching the corresponding metrics within this framework. Given the requirement of developing these metrics, this framework also defines the future research in four different directions according to the separate cost estimation sub-problems.

The software cost estimation aims to predict the most realistic effort that is required to finish a software project and so it is critical to the success of a software project management. A Software Cost Estimation affects nearly all management activities, including project bidding, resource allocation and project planning. It is affected by a number of factors, such as implementation efficiency, as well as how much the various reviews and studies completed prior to the software development stage cost. Accurate cost estimation will help us to complete the project on time and within budget. Accurate estimation is important because it has led to extensive research into the methods of software cost estimation. Some important software cost estimation methods have been studied in this research work. In addition, we have set out own criteria, which has been used to compare all the different selected methods. We have also given a score for each evaluation criteria, so that we can compare the different methods numerically for cost estimation. Our observations have shown that it is best to use a number of different estimating techniques or cost models, and then compare the results before determining the reasons for any of the large variations. None of the methods are necessarily better or worse than the others. We found, in fact, that their strengths and weaknesses often complement each other. Therefore, the main conclusion is that there is no one single technique that is best for every situation, and the results of a number of different approaches need to be carefully considered to discover what is the most likely to produce estimates that are realistic.

Accurate software cost and schedule estimation are essential for software project success. Often it referred to as the black art because of its complexity and uncertainty, software estimation is not as difficult or puzzling as people think. In fact, generating accurate estimates is straightforward -- once you understand the intensity of uncertainty and framework for the modeling process. The mystery to successful software estimation -- distilling academic information and real-world experience into a practical guide for working software professionals. Instead of arcane treatises and rigid modeling techniques, this will guide highlights a proven set of procedures, understandable formulas, and heuristics that individuals and development teams can apply to their projects to help achieve estimation proficiency with choose appropriate development approaches In the early stage of software life cycle project manager are inefficient to estimate the effort, schedule, cost estimation and its development approach. This in turn, confuses the manager to bid effectively on software project and choose incorrect development approach. That will directly effect on productivity cycle and increase level of uncertainty. This becomes a strong cause of project failure. So to avoid such problem if we know level and sources of uncertainty in model design, It will directive the developer to design accurate software cost and schedule estimation. Which are essential for software project success. However once the required efforts have estimated, little is done to recalibrate and reduce the uncertainty of the initial estimates. This paper demonstrates terminology and typology of uncertainty is presented together with a framework for the modeling process, Brief reviews have been made of 14 different (partly complementary) methods commonly used in uncertainty assessment its interaction with the broader system development process and the role of uncertainty at different stages in the modeling processes.. The applicability of these methods has been mapped according to purpose of application, stage of the modeling process and source and type of uncertainty addressed.

Software cost and effort estimation using dragonfly whale optimized multilayer perceptron neural network

Estimation models in software engineering are used to predict some important attributes of future entities such as software development effort, software reliability and programmers productivity. Among these models, those estimating software effort have motivated considerable research in recent years. Estimation by analogy is one of the attractive techniques in software effort estimation field. However, the procedure used in estimation by analogy is not yet able to handle correctly categorical data. In this paper, we propose a new approach based on reasoning by analogy, fuzzy logic and linguistic quantifiers to estimate effort when the software project is described either by categorical or numerical data. Fuzzy logic-based cost estimation models are more appropriate when vague and imprecise information is to be accounted for. Fuzzy systems try to behave just like the processes of the brain with a rule base. In our proposed method the effort and the cost can be estimated using the Fuzzy logic based analogy method and the cost can be calculated. The performance of the proposed system is calculated using MARE (Mean Absolute Relative Error).

Cost overrun of software projects is major cause of their failures. In order to facilitate accurate software cost estimation, there are several metrics, tools and datasets. In this paper, we evaluate and compare different metrics and datasets in terms of similarities and differences of involved software attributes. These metrics forecast project cost estimations based on different software attributes. Some of these metrics are public and standard while others are only employed in a particular metric tool/dataset. Sixteen public cost estimation datasets are collected and analyzed. Different perspectives are used to compare and classify those datasets. Tools for feature selection and classification are used to find the most important attributes in cost estimation datasets toward the goal of effort prediction. In order to have better estimation it is needed to correlate cost estimation from different resources, which requires a unified standard for software cost estimation metric tools and datasets. It is pertinent that a common cost estimation model may not work for each project due to diverse project size, application areas etc. We suggest having a standardized terminology of project attributes used for cost estimation. This would improve cost estimation as multiple metrics could be applied on a project without much additional effort.

Software systems of today are often complex, making development costs difficult to estimate. This paper uses data from 50 projects performed at one of the largest banks in Sweden to identify factors that have an impact on software development cost. Correlation analysis of the relationship between factor states and project costs was assessed using ANOVA and regression analysis. Ten out of the original 31 factors turned out to have an impact on software development project cost at the Swedish bank including the: number of function points, involved risk, number of budget revisions, primary platform, project priority, commissioning body's unit, commissioning body, number of project participants, project duration, and number of consultants. In order to be able to compare projects of different size and complexity, this study also considers the software development productivity defined as the amount of function points per working hour in a project. The study at the bank indicates that the productivity is affected by factors such as performance of estimation and prognosis efforts, project type, number of budget revisions, existence of testing conductor, presentation interface, and number of project participants. A discussion addressing how the productivity factors relate to cost estimation models and their factors is presented. Some of the factors found to have an impact on cost are already included in estimation models such as COCOMO II, TEAMATe, and SEER-SEM, for instance function points and software platform. Thus, this paper validates these well-known factors for cost estimation. However, several of the factors found in this study are not included in established models for software development cost estimation. Thus, this paper also provides indications for possible extensions of these models.

Accurately estimating software development costs is paramount for software project success. Global attention has focused on addressing inaccuracies in software cost estimating models leading to numerous approaches. Although machine learning algorithm techniques have witnessed improvement, the challenge of varying bias and variance from weaker learners persists, leading to the adoption of ensemble learning techniques for increased accuracy. This paper extends previous work on ensemble learning Software Cost Estimation (SCE) approach through the use of a Stacking Ensemble Learning Model for SCE (SELM-SCE) to improve the accuracy of software cost estimation. A combination of unsupervised and supervised machine learning methods were used on software projects. Two datasets (Desharnais and Maxwell) for software cost estimation were used for the model training and validation using cross-validation techniques. The proposed SELM-SCE model developed was subjected to two training models: K-Means and SMOTE technique to solve the problem of overfitting and poor performance caused by data imbalance. The performance of the proposed SELM-SCE model was first evaluated against three regression performance metrics: coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE). The results showed that the SMOTE SELM-SCE without unsupervised machine learning improves the accuracy of the model performance. The performance of SELM-SCE was compared with existing models using prediction accuracy, precision, recall, and F1-score classification performance metrics. The results from the SELM-SCE model showed remarkable improvement when compared to the existing model

Empirical software measurement is a dynamic research domain within software engineering. Measuring the software attributes during software development is crucial to ensure the successful delivery of a desired software product. It involves the measurement of multiple dynamic metrics based on software process and software product. The traditional approaches to measure the current software practices are not so appropriate due to the changing development process and increasing complexity. The changing environment requires techniques to make measurements that are adaptable to the changes. Machine learning (ML)-based techniques are prominently being deployed for measuring the software empirically. Past three decades witness the successful application of ML-based techniques for empirical measurements of software. ML is powerful enough to accurately measure the software attributes. But the synergy of ML algorithms and metaheuristic algorithm is apparently more promising in the domain of software measurement. Since 2001, metaheuristics are being fused with ML-based techniques to optimize the accuracy of the predictor based on ML. The estimation of software cost (or effort), estimation of development time (or schedule), prediction of software quality, and early detection of software anomalies are key tasks in the most popular research sector of entire field of software measurement. The vital role of metaheuristics along with ML in empirical software measurements is as follows. First, metaheuristics are used to optimize the hyperparameters of ML algorithms; second, to mitigate the problem of the curse of dimensionality of data; and third, to select the best ML model for the candidate problem. This study proposes a full-fledged description to the application of metaheuristic algorithms to empirical software measurements. The chapter begins with the introduction to the metaheuristic approach and its application to measure software empirically in conjunction with ML algorithms. Then, the state of the art of deployment of metaheuristics in the domain of software measurements is elaborated. Later, the study discusses the three major application areas of metaheuristics: hyperparameter tuning, dimensionality reduction, and model selection in detail. For each of these application areas, case studies are quoted for clearer insight into the techniques and their applications. The author proposes a novel metaheuristic technique in this chapter for feature selection. It brings more clarity for devising new methods. Then the chapter is concluded with special remarks on future scope and references made.

Chapter 8 - Software costing in practice

Software cost estimation is very crucial for the success of the software. For this reason, many studies have been conducted to identify various cost estimation techniques and barriers affecting the software estimation process in traditional development. However, these days, most software development organizations are working following agile methodologies. Therefore, there is a need to identify the critical success factors and barriers of cost estimation for agile methodology followers. In this study, a systematic literature review and survey is conducted to determine the crucial success factors and barriers of cost estimation. Therefore, this study might help an agile project manager plan the cost and effort of the software project by getting an idea from the list we have provided after validating the techniques used from the state of the art and industry so that we can come up with practical guidelines.

The software cost estimation (SCE) problem is one of the major challenges in software engineering. Inaccurate cost and time estimation in a software project may lead to devastating damage to software companies. To deal with this issue, software researchers have made significant efforts during recent years to improve and modify the available SCE models, one widely-used model of which is the Constructive Cost Model (COCOMO). This research aims to optimize the coefficients of a standard COCOMO model for SCE by combining genetic algorithm (GA) and environmental adaptation (EA) methods. The results indicate that the EA algorithm can solve the divergence issue of the genetic algorithm and optimize the coefficients of the COCOMO model as well. Moreover, the accuracy of the SCE in the case of combining GA and EA algorithms is 8% higher than when these algorithms are separately adopted.

The development of a successfull software depends upon the accurate estimation, as various factors are responsible for the overall assessment of any project. The Sofware Cost Estimation is the most complicated and challenging task in software industry. Many estimation models are introduced by the time, that concludes estimation is not a precise science and demanding of new methodologies should be proposed day by day. Software Cost Estimation of a project is vital to the acceptance or rejection of the development of software project. Various techniques have been introduced. This paper enlightens two categories of Software Cost Estimation techniques ie Algorithimic and Non Algorithimic. This paper evaluates the Algorithmic Models such as SLIM, COCOMO, Function Points and Non Algorithimic Models such as Analogy, Expert Judgement which are used to estimate software costs. Poor planning frequently leads to project failure and considerable outcomes for the project team. Software project managers should be aware of the increasing of project failures. In this paper, several existing methods for software cost estimation are illustrated and their aspects will be discussed.

The recent surge in global competition over manufacturing goods has led to a continued effort to reduce price, improve quality and reach the market faster. An important element of competitive performance is the ability to provide accurate and fast cost estimation. This frontier has been challenged from several directions including development of better activity-based cost methods, better parametric cost estimation methods and using the Web for faster and more accurate communication. This paper presents a system that uses the Web to link design stations and manufacturing shops in order to provide fast and accurate cost estimation. The system is a Web-enabled integrated environment that provides process-planning capabilities, machining time and cost estimation, and supplier selection. By utilizing Web technologies, the designers can submit the design to a central server that links to the various manufacturing shops and help them generate accurate cost estimation. The central server provides the designer with the best cost option, without compromising the manufacturers’ sensitive cost data. The prototype system is implemented using Java 2 Enterprise Edition, JSP (Java Server Page) and Enterprise Java Beans. The paper presents the system architecture and demonstrates its functionality.