Chapter 8 - Software costing in practice

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 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.

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

Playing a pivotal role in software development, the Constructive Cost Model (COCOMO) offers a systematic and structured approach to cost estimation. It stands as a widely utilized model, aiding project managers in estimating the required effort, time, and cost for software development projects. COCOMO takes into consideration diverse factors, including the project's size, complexity, and the experience of the development team. The utilization of COCOMO empowers software development teams to make informed decisions related to resource allocation, project scheduling, and budgeting. Its application extends to managing expectations, enhancing project planning, and mitigating the risk of cost overruns. The integration of machine learning assumes a critical role in advancing cost estimation within the realm of software development, specifically through COCOMO. Through the utilization of machine learning algorithms, COCOMO gains the capability to analyze and interpret extensive datasets, taking into account numerous complex factors that influence project costs. This work aims to propose a model for software cost estimation using an advanced machine learning technique, i.e. Adaptive boosting, which has improved accuracy, reduced overfitting, effectiveness with imbalanced data, and good generalization capabilities. The proposed work may contribute to the success of software projects, providing a reliable and comprehensive framework for cost estimation.

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.

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.

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.

The accuracy of software cost estimation depends on the relevancy of the cost estimation dataset, the quality of its data and its suitability for the targeted software development environment. Software development cost is impacted by technical, socio-economic and country-specific organizational and cultural environments. Current publicly available software cost estimation datasets represent environments of North America and Europe, thus limiting their application in technically and economically constrained software industries. In this paper we introduce the SEERA (Software enginEERing in SudAn) cost estimation dataset, a dataset of 120 software development projects representing 42 organizations in Sudan. The SEERA dataset contains 76 attributes and, unlike current cost estimation datasets, is augmented with metadata and the original raw data. This paper describes the data collection process, submitting organizations and project characteristics. In addition, we give a general analysis of the dataset projects to illustrate the impact of local factors on software project cost and compare the data quality of the SEERA dataset to public datasets from the PROMISE repository. The SEERA dataset fills a gap in the diversity of current cost estimation datasets and provides researchers with an opportunity to evaluate the generalization of previous and future cost estimation methods to constrained environments and to develop new techniques that are more suitable for these environments.

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.

Effective software cost and size estimation is essential for planning and resource allocation in software development. Recent empirical work which attempts to validate, or which compares, software size and cost estimation models and tools is surveyed. Some observations are made concerning the rather poor results frequently obtained and the need for calibration of models for specific development environments.

Cost estimation of maintenance phase is necessary to predict the reliability, improve the productivity, project planning, controlling and adaptability of the software. Accurate estimation makes good understanding between the customer and user. Maintenance plays important role in various software developments like Iterative development, Agile development and Component Based Software Development (CBSD). In the recent year, software development turned into engineering through the introduction of Component Based Software Development. Though there are various models to estimate the maintenance cost of traditional software like COCOMO model, SLIM, Function Point (FP) model but till now there is no such model to estimate the maintenance cost of Component Based Software Development. In this kind of situation there is needed to develop a model to estimate the maintenance cost of Component-Based Software (CBS). This paper presents proposed basic maintenance cost estimation model for Component Based Software on the basis of COCOMO. This model is based on three parameters: Development cost of Component Based Software, ACT (Annual Change Traffic), Technical and Non-Technical factors which affect the maintenance cost of Component Based Software.

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 cost estimation errors are increasing in the automotive industry along with the number of software components in modern vehicles. As the software cost estimation is an important and problematic part of project planning there is a need of process improvement to decrease estimation errors. However, to improve the process of cost estimation there is a need to explore whether the perceived cost estimation problem is an estimation problem or if it is a management problem. This paper focuses on inhibitors in the process of software cost estimation in system development projects and reports the results of a study carried out at a Swedish automotive company in order to gain an understanding for the factors that affect the cost estimation process.

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).