Effort Estimation Techniques Research Articles

Estimation of Software Development Effort: A Differential Evolution Approach

Several software effort estimation techniques based on mathematical formulations have been proposed, however there is little consensus on the best technique for effort estimation, which can predict the estimated effort optimally for any given project. Much research has been carried out in finding out the most suitable parameter values for accurate effort estimation. This research investigates the efficacy of Differential Evolution algorithms in improving the parameter values for algorithmic models like CoCoMo and CoCoMo II. Parameter values were obtained by using the three successful mutation strategies in Differential Evolution. The proposed methodology was tested on two datasets from the Promise Repository. Test results were compared with the original CoCoMo and CoCoMo II models based on MMRE. The proposed differential evolution approach predicted the estimated effort more accurately than the original models for the two datasets under consideration.

Empirical Study of Function Point Analysis during Software Development Phase

During the development of the software, Software Requirement Changes (SRC) often occurs. Software estimation helps in maintaining a balance between the increase and decrease of the price, time, etc. Function Point Analysis (FPA) is a software effort estimation technique used for measuring the size and complexity of software by calculating the functionality from a user's point of view. Function count focuses on what functionality is being delivered. It enables programmers to perform function point counting themselves instead of contacting an expert. An empirical study has been conducted to analyze the capability of the FPA technique. The results of the FPA-SDP (Function Point Analysis for Software Development Phase) model can help software project managers in (i) knowing the inconsistent states of software artifacts (ii) estimating the actual size of a change request with its complexity level for the software development phase.

Preliminary Data Examination on Effort Estimation of Resources Needed to Upload Content on Learning Management System (LMS)

Effort Estimation has been a challenging part of e-Learning due to the growing change in technology. E-Learning industry has to meet dynamic requirements of the customer. Content development has to undergo various stages during its development. During the process, initial SLA (Service Level Agreement) varies frequently and there exists several problems in content delivery. Scope Creep is the result of dynamic expectations from the clients without any limitation to the time. This affect the delivery of the product as the resources that were allocated to the development of the product were according to the initial SLA. This paper discusses about parameters that affect the estimation along with resource requirement computation. The paper further focuses on traditional effort estimation technique while analyzing the scope creep life cycle for e-Learning project. The Investigation is made by considering one of the leading middle level e-Learning organization. The case-study and the statistical analysis are carried out on the data which is collected from the company. From the analysis, amount of resource required to handle the dynamic data can be estimated.

Validation of Existing Software Effort Estimation Techniques in Context with Mobile Software Applications

In the current generation of information technology, mobile applications (apps) have become an essential and momentous source to publicize the information across the world. Academia, industries and other organizations have preferred mobile apps rather than classical software. Mobile apps are different from classical software and popularity, adaptability of mobile apps is more with wide range use. The growth of mobile apps across various fields has shown a big challenge for mobile app development industries to deliver apps on time and budget with desired accuracy and performance. Planning of mobile-based projects is a very complex task for the software industry, especially estimation of effort, time and cost for development of mobile apps. There are various literature, method, and model available in the field of classical software but mobile apps are different from classical software by their nature. It has also observed that the selection of input data is also affecting the accuracy of prediction. There is lack of calibrated model and method that administer the immense scope of determination in development of effort estimation for mobile apps. In this paper, various existing techniques of effort estimation have applied on software analytics for mobile apps (SAMOA) dataset for better analysis of suitable estimation technique that fits for mobile App development. The aim of this paper is twofold—(i) to explore the performance of variously established estimation technique on mobile app development (SAMOA dataset). (ii) Analysis of experimental results and, suggesting the best technique for the distinguished mobile app development scenario. The work is carried out adopting four techniques namely multiple linear regressions, Multi-Layer Perceptron Neural Network (MLP-NN), Genetic Algorithm (GA) and Naive forecasting approach. The results have compared with these statistical models. Among all techniques, the experimental results have presented that the GA was outperforming among four effort estimation techniques. Mobile app effort estimation models have built using four-estimation technique using SAMOA dataset. In addition, we investigated and compared various techniques namely MLP, MLP-NN, GA and Naive forecasting approach. Upon construction, accuracy measures MMRE, MRE, PRED(25) represented promising outcomes for mobile apps used in the effort estimation model construction and validation of the process. The analysis presented that GA provided better performance rather than another approach.

An Empirical Investigation of Effort Estimation in Mobile Apps Using Agile Development Process

Effort estimation is essential in order for a project manager and development team members to be able to successfully plan for a software project. The planning and development of mobile applications present many challenges. The aim of this study is to provide and report an overview on the state of the practice of effort estimation techniques that companies use for their mobile app projects. This study focuses on organisations which apply the Agile development process during their projects. We conducted structured and semi-structured interviews with 20 Agile practitioners at 18 different organisations. The results revealed that Planning Poker (PP) and Expert Judgment (EJ) were the most frequently used estimation techniques in mobile app projects.

Investigation on test effort estimation of mobile applications: Systematic literature review and survey

Abstract Context In the last few years, the exigency of mobile devices has proliferated to prodigious heights. The process of developing the mobile software/application proceeds amidst testing phase to verify the correctness of the mobile app. The estimation of testing plays a vital role in the effective completion of testing. Objective To identify how estimation of test effort for mobile applications is distinct from other software via published literature and from mobile software organizations. Second is to recognize different issues in adapting traditional test estimation methods to the mobile domain and if suggestions from survey results could be helpful in providing an improved test estimation model for mobile applications. Method A systematic literature review is conducted followed by a survey through an online questionnaire filled from experienced mobile application developers and testers. Results The results from SLR cover identification of mobile app specific characteristics and reports test effort estimation techniques in the mobile domain. Findings from survey corroborate that a) Function Point/Test Point Analysis is highly adapted traditional test estimation technique to mobile domain; b) Challenges like uncertain requirements, no tool support for test estimation, complexity in testing, client miscommunication etc. are reported; c)Suggestions to improve test estimation process include proper test planning, adoption of agile methodology, healthier communication among client, developer, and tester etc.; d) On the basis of responses, Analytical Hierarchical Process (AHP) identifies “Diverse Devices and OS” along with “Type of App” as highly influential mobile app characteristic on the test estimation process. Conclusion Results conclude that the importance of identified mobile app characteristics from SLR cannot be ignored in the estimation process of mobile software testing. There might be a possibility to improve existing test estimation techniques for mobile apps by giving weight to mobile app specific characteristics and by considering suggestions from experienced developers and testers.

MINN: A Missing Data Imputation Technique for Analogy-based Effort Estimation

Success and failure of a complex software project are strongly associated with the accurate estimation of development effort. There are numerous estimation models developed but the most widely used among those is Analogy-Based Estimation (ABE). ABE model follows human nature as it estimates the future project’s effort by making analogies with the past project's data. Since ABE relies on the historical datasets, the quality of the datasets affects the accuracy of estimation. Most of the software engineering datasets have missing values. The researchers either delete the projects containing missing values or avoid treating the missing values which reduce the ABE performance. In this study, Numeric Cleansing (NC), K-Nearest Neighbor Imputation (KNNI) and Median Imputation of the Nearest Neighbor (MINN) methods are used to impute the missing values in Desharnais and DesMiss datasets for ABE. MINN technique is introduced in this study. A comparison among these imputation methods is performed to identify the suitable missing data imputation method for ABE. The results suggested that MINN imputes more realistic values in the missing datasets as compared to values imputed through NC and KNNI. It was also found that the imputation treatment method helped in better prediction of the software development effort on ABE model.

AGS: A Precise and Efficient AI Based Hybrid Software Effort Estimation Model

To predict the amount of effort to develop software is a tedious process for software companies. Hence, predicting the software development effort remains a complex issue drawing in extensive research consideration. The success of software development process considerably depends on proper estimation of effort required to develop that software. Effective software effort estimation techniques enable project managers to schedule software life cycle activities properly. The main objective of this paper is to propose a novel approach in which an artificial intelligence (AI)-based technique, called AGS algorithm, is used to determine the software effort estimation. AGS is hybrid method combining three techniques, namely: adaptive neuro fuzzy inference system (ANFIS), genetic algorithm and satin bower bird optimisation (SBO) algorithm. The performance of the proposed method is assessed using a well standard dataset with real-time benchmark with many attributes. The major metrics used in the performance evaluation are correlation coefficient (CC), kilo lines of code (KLoC) and complexity of the software. The experimental result shows that the prediction accuracy of the proposed model is better than the existing algorithmic models.

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.

Effort estimation model for software development projects based on use case reuse

AbstractThis paper describes a new effort estimation model based on use case reuse, called the use case reusability (UCR), intended for the projects that are reusing artifacts previously developed in past projects with similar scope. Analysis of the widely spread effort estimation techniques for software development projects shows that these techniques were primarily intended for the development of new software solutions. The baseline for the new effort estimation model is the use case points model. The UCR model introduces new classification of use cases based on their reusability, and it includes only those technical and environmental factors that according to the effort estimation experts have significant impact on effort for the target projects. This paper also presents a study which validates the usage of UCR model. The study is conducted within industry and academic environments using industry project teams and postgraduate students as subjects. The analysis results show that UCR model can be applied in different project environments and that according to the observed mean magnitude relative error, it produced very promising effort estimates.

Support vector regression‐based imputation in analogy‐based software development effort estimation

AbstractMissing data (MD) is a widespread problem that can affect the ability to use data to construct effective software development effort estimation (SDEE) techniques. To deal with this challenge, several imputation techniques have been investigated in SDEE and k‐nearest neighbors (KNN)‐based imputation is still the most frequently used. To the best of our knowledge, no study has used support vector regression (SVR)‐based imputation to construct accurate estimation techniques, in particular those based on analogy. This paper introduces a new imputation technique based on SVR for handling MD in two analogy‐based SDEE techniques: classical analogy and fuzzy analogy. More specifically, we investigate whether the use of SVR instead of KNN in imputing MD improves the predictive performance of these two analogy‐based techniques. A total of 1134 experiments were conducted involving seven datasets, SVR/KNN MD imputation techniques (KNN with Euclidean and Manhattan distances), three missingness mechanisms (missing completely at random, missing at random, non‐ignorable missing), and MD percentages from 10% to 90%. The results suggest that the use of SVR imputation, rather than KNN imputation, may improve the prediction performance of both analogy‐based techniques. Furthermore, we found that the impact of MD percentage upon effort prediction performance is reduced when using SVR rather than KNN. Moreover, fuzzy analogy generates better estimates in terms of the standardized accuracy measure than classical analogy regardless of the MD technique, the dataset used, the missingness mechanism, or the MD percentage.

Towards Risk Based Effort Estimation

Studies have shown that requirement defects are among the major sources of failure constituting 32.65%. It is one of the overlooked aspects in requirements engineering and is generally considered as a potential problem that can affect the projects in a negative way. The main objective of this article is to propose a risk-based effort estimation technique that identifies, analyzes, and classifies risk at requirement engineering phase to restrain them from propagating to the later stages of the project lifecycle. This article extends the scope by integrating both threats and opportunities and their further classification based on extensive requirement analysis. The validation of the proposed approach was conducted on successfully delivered real project data. A survey is also conducted as a part of qualitative analysis for analyzing the applicability of the proposed approach. The results of the proposed method are promising and strongly support findings of literature stating that the effort needed to fix issues at a later stage in project lifecycle are costly as compared to early stages.

Developing and using checklists to improve software effort estimation: A multi-case study

Expert judgment based effort estimation techniques are widely used for estimating software effort. In the absence of process support, experts may overlook important factors during estimation, leading to inconsistent estimates. This might cause underestimation, which is a common problem in software projects. This multi-case study aims to improve expert estimation of software development effort. Our goal is two-fold: 1) to propose a process to develop and evolve estimation checklists for agile teams, and 2) to evaluate the usefulness of the checklists in improving expert estimation processes. The use of checklists improved the accuracy of the estimates in two case companies. In particular, the underestimation bias was reduced to a large extent. For the third case, we could not perform a similar analysis, due to the unavailability of historical data. However, when checklist was used in two sprints, the estimates were quite accurate (median Balanced Relative Error (BRE) bias of -0.05). The study participants from the case companies observed several benefits of using the checklists during estimation, such as increased confidence in estimates, improved consistency due to help in recalling relevant factors, more objectivity in the process, improved understanding of the tasks being estimated, and reduced chances of missing tasks.

Linear Programming as a Baseline for Software Effort Estimation

Software effort estimation studies still suffer from discordant empirical results (i.e., conclusion instability) mainly due to the lack of rigorous benchmarking methods. So far only one baseline model, namely, Automatically Transformed Linear Model (ATLM), has been proposed yet it has not been extensively assessed. In this article, we propose a novel method based on Linear Programming (dubbed as Linear Programming for Effort Estimation, LP4EE) and carry out a thorough empirical study to evaluate the effectiveness of both LP4EE and ATLM for benchmarking widely used effort estimation techniques. The results of our study confirm the need to benchmark every other proposal against accurate and robust baselines. They also reveal that LP4EE is more accurate than ATLM for 17% of the experiments and more robust than ATLM against different data splits and cross-validation methods for 44% of the cases. These results suggest that using LP4EE as a baseline can help reduce conclusion instability. We make publicly available an open-source implementation of LP4EE in order to facilitate its adoption in future studies.

Level of Confidence in Software Effort Estimation by an Intelligent Fuzzy – Neuro - Genetic Approach

Organizations are struggling to deliver the expected software functionality and quality in scheduled time and prescribed budget. Despite availability of numerous advanced effort estimation techniques overestimation and underestimation occur on a vast scale and results in project failures and significant loss to the organization. The paper proposes machine learning based approach to calculate the optimized effort and level of confidence. Genetically trained neural network evaluates the optimum effort for given COCOMO II variables. The level of confidence is evaluated by fuzzy logic and indicates the percentage that the predicted effort will not exceed the limits.

On the value of parameter tuning in heterogeneous ensembles effort estimation

Accurate estimation of software development effort estimation (SDEE) is fundamental for efficient management of software development projects as it assists software managers to efficiently manage their human resources. Over the last four decades, while software engineering researchers have used several effort estimation techniques, including those based on statistical and machine learning methods, no consensus has been reached on the technique that can perform best in all circumstances. To tackle this challenge, Ensemble Effort Estimation, which predicts software development effort by combining more than one solo estimation technique, has recently been investigated. In this paper, heterogeneous ensembles based on four well-known machine learning techniques (K-nearest neighbor, support vector regression, multilayer perceptron and decision trees) were developed and evaluated by investigating the impact of parameter values of the ensemble members on estimation accuracy. In particular, this paper evaluates whether setting ensemble parameters using two optimization techniques (e.g., grid search optimization and particle swarm) permits more accurate estimates of SDEE. The heterogeneous ensembles of this study were built using three combination rules (mean, median and inverse ranked weighted mean) over seven datasets. The results obtained suggest that: (1) Optimized single techniques using grid search or particle swarm optimization provide more accurate estimation; (2) in general ensembles achieve higher accuracy than their single techniques whatever the optimization technique used, even though ensembles do not dominate over all single techniques; (3) heterogeneous ensembles based on optimized single techniques provide more accurate estimation; and (4) generally, particle swarm optimization and grid search techniques generate ensembles with the same predictive capability.

EFFORT ESTIMATION TECHNIQUES FOR WEB APPLICATION DEVELOPMENT: A REVIEW

In present era of information technology web applications have become an indispensable and most pivotal source to disseminate information across geographically distant nodes. Most of the organisation public or private have switched their existence from traditional software based systems to web based software systems. Many characteristic differences between traditional software and web based software application made web based existence more popular with wide range adoption. The sudden surge in web application usability across different organisation have posed a grave challenge for web development companies to deliver their products onetime and within budget with much expected accurately, efficiency and reliability. Effort estimation is main domain that decides the budgetary constraints associated with web application development. Accurate estimates are very essential for both developer and client. Accurate estimates make web development companies to maintain their reputation, competitive edge and client base. Inaccuracy in effort estimates results to lose revenue, client base and competitive strength. Effort estimation is very import for every development organisation to estimate the development work in early stages of development to prove efficient, accurate and much reliable budget estimates. This paper is an effort to explore various effort estimation techniques for web development, their challenges and measures to predict accurate efforts.

EFFORT ESTIMATION OF OBJECT ORIENTED SYSTEM USING STOCHASTIC TREE BOOSTING TECHNIQUE

Effort Estimation is one of the necessary and daunting tasks in software engineering. Effort Estimation means to predict the effort required to develop the software project. Predicting the effort with high precision is an ultimatum that draws the concern of researchers. In a need to develop best products within proper schedule, the work of proper effort estimation is of basic necessity. No doubt, there are a lot of effort estimation techniques which are already developed like COCOMO (Cost Constructive Model) etc. but these effort estimation techniques have sustained unsuitable for estimation of object oriented software because they are used for procedural programming concept. Presently, object oriented concept is frequently used in practice and as Class is the base of object oriented design so the use of Class Point approach(CPA) to estimate the effort supports the estimator in a much better way. The performance of model obtained using CPA can be upgraded by applying Stochastic Tree Boosting (STB) technique over forty project dataset collected from different sources in order to improve its prediction accuracy.

On the application of search-based techniques for software engineering predictive modeling: A systematic review and future directions

Software engineering predictive modeling involves construction of models, with the help of software metrics, for estimating quality attributes. Recently, the use of search-based techniques have gained importance as they help the developers and project-managers in the identification of optimal solutions for developing effective prediction models. In this paper, we perform a systematic review of 78 primary studies from January 1992 to December 2015 which analyze the predictive capability of search-based techniques for ascertaining four predominant software quality attributes, i.e., effort, defect proneness, maintainability and change proneness. The review analyses the effective use and application of search-based techniques by evaluating appropriate specifications of fitness functions, parameter settings, validation methods, accounting for their stochastic natures and the evaluation of developmental models with the use of well-known statistical tests. Furthermore, we compare the effectiveness of different models, developed using the various search-based techniques amongst themselves, and also with the prevalent machine learning techniques used in literature. Although there are very few studies which use search-based techniques for predicting maintainability and change proneness, we found that the results of the application of search-based techniques for effort estimation and defect prediction are encouraging. Hence, this comprehensive study and the associated results will provide guidelines to practitioners and researchers and will enable them to make proper choices for applying the search-based techniques to their specific situations.

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.