Approach For Effort Estimation Research Articles

A Systematic Literature Review on Reasons and Approaches for Accurate Effort Estimations in Agile

Background : Accurate effort estimation is crucial for planning in Agile iterative development. Agile estimation generally relies on consensus-based methods like planning poker, which require less time and information than other formal methods (e.g., COSMIC) but are prone to inaccuracies. Understanding the common reasons for inaccurate estimations and how proposed approaches can assist practitioners is essential. However, prior systematic literature reviews (SLR) only focus on the estimation practices (e.g., [26, 127]) and the effort estimation approaches (e.g., [6]). Aim : We aim to identify themes of reasons for inaccurate estimations and classify approaches to improve effort estimation. Method : We conducted an SLR and identified the key themes and a taxonomy. Results : The reasons for inaccurate estimation are related to information quality, team, estimation practice, project management, and business influences. The effort estimation approaches were the most investigated in the literature, while only a few aim to support the effort estimation process. Yet, few automated approaches are at risk of data leakage and indirect validation scenarios. Recommendations : Practitioners should enhance the quality of information for effort estimation, potentially by adopting an automated approach. Future research should aim to improve the information quality, while avoiding data leakage and indirect validation scenarios.

Improving effort estimation of software products by augmenting class point approach with regression analysis

Software products are essential parts of many organizations on-going business up to a large extent. The main factors contributing to the successful delivery of a software product are its timely completion within the allocated budget and its quality compliance. Customer goodwill and profitability are very important for a software organization’s continued business. A large proportion of software products are delivered late or go over-budget causing significant inconvenience to the customers. This work proposes an accurate development effort estimation approach for software products. The Class Point (CP) approach with regression analysis method has been used for estimation of the development effort. This work uses a two step estimation approach. In the first step, an enhanced CP approach is used to evaluate the development effort of the system. In the second step, regression analysis models are utilized to refine the estimated effort accuracy. The results derived by applying the proposed two step approach confirmed the validity and the accuracy of this approach. It was observed that the SVR with RBF kernel is providing the best accuracy compared to other approaches.

A Novel Effort Estimation Approach for Migration of SOA Applications to Microservices

Microservices architecture's popularity is rapidly growing as it eases the design of enterprise applications by allowing independent development and deployment of services. Due to this paradigm shift in software development, many existing Service Oriented Architecture (SOA) applications are being migrated to microservices. Estimating the effort required for migration is a key challenge as it helps the architects in better planning and execution of the migration process. Since the designing style and deployment environments are different for each service, existing effort estimation models in the literature are not ideal for microservice architecture. To estimate the effort required for migrating SOA application to microservices, we propose a new effort estimation model called Service Points. We define a formal model called service graph which represents the components of the service based architectures and their interactions among the services. Service graph provides the information required for the estimation process. We recast the use case points method and model it to become suitable for microservices architecture. We have updated the technical and environmental factors used for the effort estimation. The proposed approach is demonstrated by estimating the migration effort for a standard SOA based web application. The proposed model is compatible with the design principles of microservices and provides a systematic and formal way of estimating the effort. It helps software architects in better planning and execution of the migration process.

Software Testing Effort Estimation and Related Problems

Although testing effort estimation is a very important task in software project management, it is rarely described in the literature. There are many difficulties in finding any useful methods or tools for this purpose. Solutions to many other problems related to testing effort calculation are published much more often. There is also no research focusing on both testing effort estimation and all related areas of software engineering. To fill this gap, we performed a systematic literature review on both questions. Although our primary objective was to find some tools or implementable metods for test effort estimation, we have quickly discovered many other interesting topics related to the main one. The main contribution of this work is the presentation of the testing effort estimation task in a very wide context, indicating the relations with other research fields. This systematic literature review presents a detailed overview of testing effort estimation task, including challenges and approaches to automating it and the solutions proposed in the literature. It also exhaustively investigates related research topics, classifying publications that can be found in connection to the testing effort according to seven criteria formulated on the basis of our research questions. We present here both synthesis of our finding and the deep analysis of the stated research problems.

The nature of build changes

Build systems are an essential part of modern software projects. As software projects change continuously, it is crucial to understand how the build system changes because neglecting its maintenance can, at best, lead to expensive build breakage, or at worst, introduce user-reported defects due to incorrectly compiled, linked, packaged, or deployed official releases. Recent studies have investigated the (co-)evolution of build configurations and reasons for build breakage; however, the prior analysis focused on a coarse-grained outcome (i.e., either build changing or not). In this paper, we present BuildDiff, an approach to extract detailed build changes from Maven build files and classify them into 143 change types. In a manual evaluation of 400 build-changing commits, we show that BuildDiff can extract and classify build changes with average precision, recall, and f1-scores of 0.97, 0.98, and 0.97, respectively. We then present two studies using the build changes extracted from 144 open source Java projects to study the frequency and time of build changes. The results show that the top-10 most frequent change types account for 51% of the build changes. Among them, changes to version numbers and changes to dependencies of the projects occur most frequently. We also observe frequently co-occurring changes, such as changes to the source code management definitions, and corresponding changes to the dependency management system and the dependency declaration. Furthermore, our results show that build changes frequently occur around release days. In particular, critical changes, such as updates to plugin configuration parts and dependency insertions, are performed before a release day. The contributions of this paper lay in the foundation for future research, such as for analyzing the (co-)evolution of build files with other artifacts, improving effort estimation approaches by incorporating necessary modifications to the build system specification, or automatic repair approaches for configuration code. Furthermore, our detailed change information enables improvements of refactoring approaches for build configurations and improvements of prediction models to identify error-prone build files.

A pragmatic ensemble learning approach for effective software effort estimation

The immense increase in software technology has resulted in the convolution of software projects. Software effort estimation is fundamental to commence any software project and inaccurate estimation may lead to several complications and setbacks for present and future projects. Several techniques have been following for ages of the software effort estimation. As the application of software is extensively increased in its size and complexity, the traditional methods aren’t adequate to meet the requirements. To achieve the accurate estimation of software effort, in this paper, a gradient boosting regressor model is proposed as a robust approach. The performance is compared with regression models such as stochastic gradient descent, K-nearest neighbor, decision tree, bagging regressor, random forest regressor, Ada-boost regressor, and gradient boosting regressor by employing COCOMO’81 containing 63 projects and CHINA of 499 projects. The regression models are evaluated by the evaluation metrics such as MAE, MSE, RMSE, and R2. From the results, it is evident that the gradient boosting regressor model is performing well by obtaining an accuracy of 98% with COCOMO’81 and 93% with CHINA dataset. The proposed method significantly performs better than all regression models used in comparison with both the datasets.

Software development effort estimation: a systematic mapping study

The field of software-development effort estimation explores ways of defining effort through prediction approaches. Even though this field has a crucial impact on budgeting and project planning in industry, the number of works classifying and examining currently available approaches is still small. This article, therefore, presents a comprehensive overview of these approaches, and pinpoints research gaps, challenges and trends. A systematic mapping of the literature was designed and performed based on well-established practical guidelines. In total, 120 primary studies were selected, analysed and categorised, after applying a careful filtering process from a sample of 3746 candidate studies to answer six research questions. Over 70% of the selected studies adopted multiple effort estimation approaches; over 45% adopted evaluation research as a research method; over 90% of the participants were students, rather than professionals; most studies had their quality assessed as high, and were most commonly published in journals. Our study benefits practitioners and researchers by providing a body of knowledge about the current literature, serving as a starting point for upcoming studies. This article reports challenges worth investigating, regarding the use of cognitive load and team interaction.

Effort Estimation Approach through Extracting Use Cases via Informal Requirement Specifications

Sometimes unclearly describing the requirement specifications of satisfied customer’s needs, means it may be difficult to develop the production of high-quality software systems. A persistent issue of requirement engineering is how to clearly understand the requirements of the large and complex software project, and also how to analyze them exactly. To solve this problem, we propose a linguistic analysis method based on the semantic analysis of the Fillmore’s textual approach. This method extracts use-cases from informal requirement specifications. For applied requirement engineering with this method, we suggest extracting a use-case diagram, as well as calculating the software effort estimation with the original use-case point (UCP). To simply explanations of our use-case extraction method, we use one example of a simple postal information system.

Inclusion of Functional and Non-Functional Parameters for the Prediction of Overall Efforts of Mobile Applications.

Size is a major and main parameter for the estimation of efforts and cost of software applications in general and mobile applications in particular and estimating effort, cost and time has been a key step in the life cycle of the software project. In order to create a sound schedule for the project, it is therefore important to have these estimates as soon as possible in the software development life cycle. In past years, many methods have been employed to estimate size and efforts of mobile applications but till now these methods do not meet the expected needs from customer. In this paper, we present a new size measurement method i.e., Mobile COSMIC Function Points (MCFP) based on the COSMIC approach, which is a primary factor for estimation of efforts in mobile application development. This paper analyzes the possibility of using a combination of Functional and Non-functional parameters including both Mobile Technical Complexity Factors (MTCF) and Mobile Environmental Complexity Factors (MECF) for the purpose of mobile application sizing prediction and hence effort estimation. For the purpose of this study, thirty six mobile applications were analyzed and their size and efforts were compared by applying the new effort estimation approach. In this context of a mobile application, few investigations have been performed to compare the effectiveness of COSMIC, FP's and the proposed approach “COSMIC Plus Effort Estimation Model (CPEEM)”. The main goal of this paper is to investigate if the inclusion of Non functional parameters imposes an effect on the functional size of mobile application development. Upon estimating efforts using the proposed approach, the results were promising for mobile applications when compared the results of our approach with the results of the other two approaches

A fuzzified story point approach for agile projects

Effort estimation is very important for any software development organisation. A lot of work is present for effort estimation in traditional software development but, effort estimation in agile software development (ASD) poses a great research challenge as not much of work is done in this regard. This work proposes a fuzzy approach for effort estimation in ASD environment by identifying first the cost drivers affecting the effort of agile projects and then calculating the effort using fuzzified story point approach. These cost drivers are collected through meetings and by taking a web survey from the companies in Delhi-NCR which practise ASD. Statistical tests are also performed in order to show the dependency of effort on these cost drivers. Here, fuzzy interval type-2 is used which is compared with fuzzy type-1. It is found that, the effort calculated using fuzzy interval type-2 is better than fuzzy type-1.

A fuzzified story point approach for agile projects

Effort estimation is very important for any software development organisation. A lot of work is present for effort estimation in traditional software development but, effort estimation in agile software development (ASD) poses a great research challenge as not much of work is done in this regard. This work proposes a fuzzy approach for effort estimation in ASD environment by identifying first the cost drivers affecting the effort of agile projects and then calculating the effort using fuzzified story point approach. These cost drivers are collected through meetings and by taking a web survey from the companies in Delhi-NCR which practise ASD. Statistical tests are also performed in order to show the dependency of effort on these cost drivers. Here, fuzzy interval type-2 is used which is compared with fuzzy type-1. It is found that, the effort calculated using fuzzy interval type-2 is better than fuzzy type-1.

A systematic literature review on crowdsourcing in software engineering

BackgroundCrowdsourcing outsources a task to large groups of people by open call format, and it recently plays significant role for software practitioners. AimThe purpose of this study is to conduct a comprehensive overview on crowdsourcing in software engineering (CSE), concerning business models, tools, platforms, software development processes, and software economics. MethodWe conducted a systematic literature review on CSE. We identified 158 relevant studies and 6 secondary studies. We further reviewed 67 primary studies that passed our quality assessment criteria. We defined 10 research questions and synthesized different approaches used in primary studies regarding each question. ResultsMajority of studies report the application of crowdsourcing for coding and testing tasks. Crowdsourcing follows a unique methodology in which project planning, task specification and deployment have more emphasis. There is not enough literature on effort estimation approaches in CSE and associated cost factors. Complexity of the task and its expected duration play significant role in estimation. ConclusionsFuture studies should focus more on economic models, experience reports, specific software development methodologies, and strategic pricing mechanism for CSE.

Empirical evaluation of an entropy‐based approach to estimation variation of software development effort

AbstractAs effort estimation has gained increasing attention, most of the techniques proposed have focused on the accuracy of effort estimates. Yet no clear conclusions have been drawn on which techniques perform best in all contexts. We propose an entropy‐based approach to effort estimate variation caused by measurement and model error sources whatever the effort estimation technique used. The proposed approach was empirically evaluated by exploring three entropy formulae, four interpolation methods, and two analogy‐based effort estimation approaches (crisp and fuzzy analogy) over seven datasets using the Jackknife evaluation method.The obtained results show that the three entropy formulae have in general the same positive influence on the performance of the entropy‐based approach measured in terms of absolute error of effort deviation. In addition, the spline interpolation outperformed all other interpolation methods, using any of the entropy formulae. Moreover, achievement percentages of the best variants of our approach closely approximated those of the Gaussian distribution confirming that the Gaussian distribution is useful for characterizing effort estimate variation.

Estimated software measurement base on use case for online admission system

This article aims to measure the size of the software Online Admission System using the method of Use Case Point (UCP). UCP method can help developers in the estimation of factor productivity development of software projects that include estimated time work, human resource needs, and the estimated cost of creating software. This method has the ability in providing the effort estimation approach which is needed to do a software development project. The measurement is carried out based on the number and complexity of software projects by analyzing the actor, use case, and a variety of technical factors as well as environmental factors. The result of the measurement is the software development project of online admission system belongs the medium category with the acquisition value of UCP 103.86, estimated cost 277,168,750 IDR and time work 1,454 hours

Uncertainty management in software effort estimation using a consistent fuzzy analogy-based method

Software effort estimation is a critical task in software project development management. Unfortunately, the uncertainty and inaccuracy are inherent properties of the software effort estimation environment. These are caused by the limited capabilities of the managers, to foresee, measure and describe factors influencing the software effort. The promising Fuzzy Analogy-based Software Effort Estimation model (FASEE) employs successfully fuzzy logic with approximate reasoning theory to handle imprecision and reasoning under uncertainty. Also, FASEE use possibility distribution to quantify the uncertainty in the estimate that aid the software managers to assess risks. Yet, the FASEE suffer from the low data quality and the uncertainty induced in the reasoning process. In this paper, we propose an enhancement of the FASEE, by imposing consistency criteria to deal with the aforementioned drawbacks. So, the underlying model, called Consistent Fuzzy Analogy-based Software Effort Estimation (C-FASEE) is endowed with two capabilities. The first one introduces consistency criteria in attribute representation by fuzzy sets to enable fitting each attribute to the software effort. The second one introduces a new relation of confidence to measure the extent that the resulted most similar projects respect the assumption “similar projects have similar efforts”. Moreover, the C-FASEE method provide a fuzzy estimate of the most possible fuzzy set will the true effort of the new software project falls in. This allow to the software manager to assess risks more optimally. The proposed C-FASEE is validated over thirteen software project datasets that represent different complexities. The obtained results are compared to variant methods of the analogy-based software effort estimation approach. The experimental results show that our proposal provides a good estimation accuracy of and has significantly best performance against the comparison methods.

An effective approach for software project effort and duration estimation with machine learning algorithms

During the last two decades, there has been substantial research performed in the field of software estimation using machine learning algorithms that aimed to tackle deficiencies of traditional and parametric estimation techniques, increase project success rates and align with modern development and project management approaches. Nevertheless, mostly due to inconclusive results and vague model building approaches, there are few or none deployments in practice.The purpose of this article is to narrow the gap between up-to-date research results and implementations within organisations by proposing effective and practical machine learning deployment and maintenance approaches by utilization of research findings and industry best practices. This was achieved by applying ISBSG dataset, smart data preparation, an ensemble averaging of three machine learning algorithms (Support Vector Machines, Neural Networks and Generalized Linear Models) and cross validation. The obtained models for effort and duration estimation are intended to provide a decision support tool for organisations that develop or implement software systems.

Which models of the past are relevant to the present? A software effort estimation approach to exploiting useful past models

Software Effort Estimation (SEE) models can be used for decision-support by software managers to determine the effort required to develop a software project. They are created based on data describing projects completed in the past. Such data could include past projects from within the company that we are interested in (WC projects) and/or from other companies (cross-company, i.e., CC projects). In particular, the use of CC data has been investigated in an attempt to overcome limitations caused by the typically small size of WC datasets. However, software companies operate in non-stationary environments, where changes may affect the typical effort required to develop software projects. Our previous work showed that both WC and CC models of the past can become more or less useful over time, i.e., they can sometimes be helpful and sometimes misleading. So, how can we know if and when a model created based on past data represents well the current projects being estimated? We propose an approach called Dynamic Cross-company Learning (DCL) to dynamically identify which WC or CC past models are most useful for making predictions to a given company at the present. DCL automatically emphasizes the predictions given by these models in order to improve predictive performance. Our experiments comparing DCL against existing WC and CC approaches show that DCL is successful in improving SEE by emphasizing the most useful past models. A thorough analysis of DCL’s behaviour is provided, strengthening its external validity.

A critical review on effort estimation techniques for service-oriented-architecture-based applications

In the recent past, Service Oriented Architecture (SOA) has become very popular as it allows legacy systems to remain mostly unchanged and provide functionalities in the form of services. In contrast to traditional software application development, complexity of SOA application is much higher, making the effort estimation very difficult. Hence, existing effort estimation techniques and approaches are inadequate to address the complex SOA systems. In this literature review, distinct research directions for SOA-specific effort estimation are critically examined with the help of proposed review framework with nine point agenda that include the class, structure, purview, objective, technique, value added, flip side, inference, limitations, and future research scope. In the planning phase of a software project, estimating the effort is very critical and crucial to manage the risk of cost blowout happened during the development. Not much research has been available on effort estimation for SOA applications. However, there are some good frameworks and methods provided by researchers that make a solid foundation of effort estimation for SOA applications. Available effort estimation techniques for SOA applications are analyzed by critically examining their upside and flip-side.

Optimised class point approach for software effort estimation using adaptive neuro-fuzzy inference system model

Optimised class point approach for software effort estimation using adaptive neuro-fuzzy inference system model

An Approach for Effort Estimation of Service Oriented Architecture (SOA) Projects

In the last few decades SOA (Service Oriented Architecture) has become the new trend in the IT industry. Many organizations tend to migrate to SOA in order to cope with the rapidly changing business. Effort estimation of SOA projects has become a real challenge to project managers due to the limited literatures addressing this issue. The traditional effort estimation techniques do not fit SOA projects entirely, as SOA has unique characteristics were not addressed by the traditional cost estimation approaches. These unique SOA characteristics include: loose coupling, reusability, composability and discoverability. On the other hand, cost estimation approaches that were proposed to estimate SOA projects, are still immature and most of them are impractical. They cannot be used in real life projects, as they are more guidelines than actual practical cost estimation approaches. This paper proposes an effort estimation approach for SOA projects that has been applied to different variety of services. It considers SOA characteristics and the various cost factors for different types of services including available, migrated, new and composed services. This proposed approach provides effort estimation technique for each type of service. The proposed approach also gives effort distribution among project phases for easily resources allocation. This framework has been applied to real life projects in the IT industry as the SOA project is divided into its component services and each service is estimated solely based on its type. The services' efforts are then aggregated to calculate the project’s overall effort. The estimated effort relative error in the case studies ranges from 3.66 % and 19.14%.