Accurate Effort Estimation Research Articles

Development of software effort estimation using a non-fuzzy model

Nowadays, accurate estimation of the software effort is a challenging issue for the modern software developers. To bind a contract depends purely on the estimated cost of the software. Overestimation or underestimation leads to a loss or gain of the software project and also increases the probability of success and failure of the project. In this paper we used a non fuzzy conditional algorithm to build a suitable model to estimate the software effort taking NASA software projects data. We have planned to set of linear conditional models using the domain of possible kilo lines of code (KLOC). The performance of the developed model has been analysed using NASA data set (Bailey and Basili, 1981) and we compare with the result of COCOMO tuned-PSO, Halstead, Walston-Felix, Bailey-Basili and Doty models were provided.

Applicability of the software cost model COCOMO II to HPC projects

The complexity of parallel computer architectures continuously increases with the pursuit of exaflop computing, which makes accurate development effort estimation and modelling more important than ever. While sophisticated cost models are widely used in traditional software engineering, they have rarely been investigated for the performance-oriented HPC domain. Therefore, we evaluate the fit and accuracy of the popular COCOMO II model to HPC setups. We lay out a general methodology to evaluate HPC projects with COCOMO II and analyse its cost parameters for the investigated parallelisation projects with OpenACC on NVIDIA GPUs. Further, we evaluate the accuracy of the model in comparison to the reported efforts of the projects and investigate the impact of inaccuracies in the cost parameter ratings by means of a global sensitivity analysis.

Peningkatan Akurasi Estimasi Usaha dan Biaya COCOMO II Berdasarkan Gaussian dan BCO

An accurate effort and cost estimation provides good management for software projects. Less accurate estimation will affect the management of the software project and cause the ineffectiveness of the project development process. The addition of cost driver, introduced by Barry Boehm in 2000, is used in this paper to provide better accuracy, because it has covered the entire section in the estimation. However, in this paper, the accuracy of effort and cost estimation by COCOMO II Fuzzy Gaussian method is still far from actual effort. Therefore, the accuracy can still be increased using Bee Colony Optimization (BCO), as seen in the MMRE loyal results. The value of parameter A and B on COCOMO II is also changed with the initial gradual of 0.01 to give optimal value on a certain gradual. Based on the result of the implementation, the error accuracy of effort estimation and software project cost is reduced by 38%, compared to previous research. In conclusion, the proposed method can increase the accuracy of effort and cost estimation.

OPTIMIZED MODEL FOR SOFTWARE EFFORT ESTIMATION USING COCOMO-2 METRICS WITH FUZZY LOGIC

Effort estimation is the crucial activity during the planning phase of any project. Successful delivery of the software project directly dependent on the accuracy of software effort estimation in planning phase. As effort multiplier have significant influence on the COCOMO-II and this research proposed the model for improving the precision of effort estimation using fuzzy logic on COCOMO-II effort multipliers. Fuzzy Logic is a rule based architecture which runs on binary pattern. It has Input set, associated with rule-sets based on the membership function. There are three membership functions i.e. Triangular Membership function, Trapezoidal Membership Function and Bell Membership Function which has been utilized in the proposed architecture.

COMPARISION OF MAINTENANCE ACTIVITY FOR EFFORT ESTIMATION IN OPEN SOURCE SOFTWARE PROJECTS

Software estimation accuracy is amongst the biggest challenges for software developers. The most significant activity in software project management is Software development effort prediction. Many models have been proposed to make software effort estimations, but still no single model can predict the effort accurately. The demand for accurate effort estimation in the software industry is still a challenge. Accurate, precise and reliable estimates of effort at early stages of project development hold great importance for the management to meet the competitive demands of today's world. Software cost estimation is one of the most crucial tasks and predicts the effort and development time needed to develop a software system. It helps the software industries to manage their software development process efficiently. In this paper, we introduce an approach to building an effort estimation model for Open Source Software. For this purpose, effort data is mined from the developer's bug fix activities history. Our approach determines the actual time spend to fix a bug and considers it as an estimated effort. We propose an artificial neural-network-based approach to predict the amount of effort and development time of developers required for bug resolution. This paper investigates the use of Back-Propagation Neural networks for software effort estimation. The primary purpose of this paper is to estimate the software development effort using Artificial Neural-Network based techniques to improve accuracy of developers for bug resolution.

A Fuzzy based Model for Effort Estimation in Scrum Projects

This paper aims to utilize the fuzzy logic concepts to improve the effort estimation in Scrum framework and in turn add a significant enhancement to Scrum. Scrum framework is one of the most popular agile methods in which the team accomplishes their work by breaking down the work into a series of sprints. In Scrum, there are many factors that have a significant influence on the effort estimation of each task in a Sprint. These factors are: Development Team Experience, Task Complexity, Task Size, and Estimation Accuracy. These factors are usually presented using linguistic quantifiers. Therefore, this paper utilizes the fuzzy logic concepts to build a fuzzy based model that can improve the effort estimation in Scrum framework. The proposed model includes three components: fuzzifier, inference engine, and defuzzifier. In addition, the proposed model takes into consideration the feedback that is resulted from comparing the estimated effort and the actual effort. The researcher designed the proposed model using MATLAB. The proposed model is applied on three Sprints of a real software development project to present how the proposed model works and to show how it becomes more accurate over time and gives a better effort estimation. In addition, the Scrum Master and the development team can use the proposed model to monitor the improvement in effort estimation accuracy over the project life.

Early Test Effort Prediction using UML Diagrams

<p>Software Testing is essential for delivering quality software products. Accurate test effort estimation is an important part of project planning. Prediction of test effort at early phases of Software Developmen Life Cycle enables managers to plan ahead for the testing phase. This increases the chances of the project meeting its timeline while achieving required quality. Unified Modeling Language is a widely-used standard for representing Object Oriented Systems. Use Case and Class Diagrams are two of the most important models of the system created during early phases of software development. This work proposed that the initial test effort is predicted early using Use Case Diagram during the requirement stage and is refined during design stage when Class Diagrams are available. The predicted test efforts in both stages are functions of parameters derived from Use Case and Class Diagrams. Estimations can be revised when rest of the UML diagrams are created.</p>

K-Means Clustering Algorithms to Compute Software Effort Estimation

Software development effort estimation is the way of predicting the effort to improve software economics. Accurate estimation of effort is the most tedious tasks in software projects. However, several methods are used to estimate the software development effort accurately. Imprecise estimation can leads to project failure due to uncertain data. In this paper, a hybrid model based on combination of Particle Swarm Optimization (PSO), K-means clustering algorithms, neural network and ABE method is proposed. The proposed method can be useful to predict better clustering and more accurate estimation and hence, there are difficulties in clustering and outliers in the software projects. The obtained results showed the better clustering result which provides the estimation result accurately. Then, neural network and Analogy methods are used which enhance the accuracy significantly.

Regression Analysis Based Software Effort Estimation Method

Estimating the development effort of a software project in the early stages of the software life cycle is a significant task. Accurate estimates help project managers to overcome the problems regarding budget and time overruns. This paper proposes a new multiple linear regression analysis based effort estimation method, which has brought a different perspective to the software effort estimation methods and increased the success of software effort estimation processes. The proposed method is compared with standard Use Case Point (UCP) method, which is a well-known method in this area, and simple linear regression based effort estimation method developed by Nassif et al. In order to evaluate and compare the proposed method, the data of 10 software projects developed by four well-established software companies in Turkey were collected and datasets were created. When effort estimations obtained from datasets and actual efforts spent to complete the projects are compared with each other, it has been observed that the proposed method has higher effort estimation accuracy compared to the other methods.

Improving Efficiency of Fuzzy Models for Effort Estimation by Cascading & Clustering Techniques

The main challenge in software industry is the process of estimating the cost required for the development or maintenance of a project. Various models have been proposed for constructing a relationship between size of software and its development effort. Various algorithmic cost estimation models exists with their own pros and cons for estimation. Now a days, attention has turned towards Machine learning techniques as few of the problems associated with previous models are being addressed by the soft computing techniques. But, the need for accurate effort estimation in software project management is still a challenge. The literature shows the usage of Fuzzy Logic Controller for Software Effort Estimation, but the computational time is very high as the rulebase is large. The main aim is to reduce the rulebase and improve the efficiency by cascading of Fuzzy Logic Controllers. A case study on NASA 93 dataset is taken for this purpose. The Proposed work is carried out by cascading the Fuzzy Logic Controllers in a stage of two and six for Software Effort Estimation. By increasing the cascading of Fuzzy Logic Controllers, the efficiency has been improved with the minimized size in rulebase. The limitation to this process is to find out the correct number of cascaded Fuzzy Logic Controllers. To overcome this a Fuzzy Model using Subtractive Clustering has been proposed. The rulebase of the models developed by using Subtractive Clustering is further reduced. Considering the rule minimization and the various criteria for assessment, Fuzzy Models developed using Subtractive Clustering provides better software development effort estimates.

Integrating Non-Functional Requirements Into a Case Based Reasoning Model for Software Effort Estimation

The importance of accurate effort estimates is increasing in the software development industry. As a result, many methods have been proposed in order to deal with the inaccuracy and imprecision of software effort estimation. However, the most of them focus on functional requirements (FR), while non-functional requirements (NFRs) are often ignored or reflect only a very high level of features required, causing the estimates to be increasingly inaccurate. Therefore, there is a need to better understand and include all the characteristics of the software to be measured. In this paper, we are going to apply machine learning using case based reasoning (CBR) model, combined with COSMIC, to improve the precision of the estimates. This hybrid technique uses COSMIC to measure the functional size of FRs, takes into account the relationships between FRs and NFRs when measuring the impact of NFRs on the effort of FRs with which they are associated. The relationships existing between FRs and NFRs are identified in the link requirements model proposed. We aim, by this combination, to reduce the uncertainty of estimates by including all types of requirements in the measurement process.

A differential evolution-based model to estimate the software services development effort

Accurate estimation of software service development effort is a great challenge both in industry and for academia. The concept of effort is an important and effective parameter in process development and software service management. The reliable estimation of effort helps the project managers to allocate the resources better and manage cost and time so that the project will be finished in the determined time and budget. One of the most popular effort estimation methods is analogy-based estimation ABE to compare a service with similar historical cases. Unfortunately, ABE is not capable of generating accurate results unless determining weights for service features. Therefore, this paper aims to make an efficient and reliable model through combining ABE method and differential evolution algorithm to estimate the software services development effort. In fact, the differential evolution algorithm was utilized for weighing features in the similarity function of the ABE method. This weighing process could help determining the importance level of the various service features and extracting the best similar historical case. The proposed hybrid model has been evaluated on two real datasets and two artificial datasets. The obtained results were compared with common effort estimation methods. This comparison showed more accuracy, faster convergence, and lower cost of the proposed model. Copyright © 2015 John Wiley & Sons, Ltd.

Analysis of task effort estimation accuracy based on use case point size

The use case point (UCP) method is one of the most commonly used size estimation methods in software development. Applicability of UCP size for the project effort estimation is thoroughly investigated; however, little attention is devoted to the effort estimation of particular task types. The authors have created and cross-compared prediction models for estimating task-type efforts by means of UCP size using an Online analytical processing model and R packages on a set of 32 real-world projects, with the goal of facilitating analysis of the correlation between project sizes and effort required to complete task types. Requirements, scoping, functional specification, and functional testing task types have up to two times better estimation accuracies than project effort. Implementation has slightly better accuracy than the project effort, while the other task types are not correlated to the UCP size. Using estimates of the most correlated task types and other techniques, such as expert judgment for others, we improved the overall project effort prediction accuracy and decreased the error from 26 to 16%.

Feasibility and Evaluation of Probabilistic On‐site Surveys for Estimating Angler Use and Catch in High Mountain Lakes

AbstractEstimating angling effort and catch at high mountain lakes can be difficult due the abundance, remoteness, and diffuse nature of angling effort that typify high mountain lake fisheries. In this study, a simulation was used to evaluate the accuracy of catch and effort estimates derived using on‐site access–access and roving–access creel surveys at a complex of 35 high mountain lakes. Five levels of angling effort and catch at the 35 lakes were simulated, and effort varied from 3,278 to 68,741 h and catch varied from 1,737 to 50,525 fish over the duration of the season. Access–access creel surveys had an average of 32% relative error and roving–access surveys had an average of 17% relative error in estimates of aggregate (i.e., at all 35 lakes) angling effort and catch when one creel surveyor was used. Estimates were relatively robust to temporal and spatial changes in patterns of effort and catch rate over the duration of the season. Relative error was inversely related to the amount of angling effort, catch, and creel surveyors. Roving–access surveys outperformed access–access surveys at all levels of sampling effort. Bias and relative error of estimates of angling effort and catch were greater at the individual‐lake scale than when estimates were derived for all 35 lakes. Results of this study suggest that on‐site surveys can provide relatively accurate estimates of angling effort and catch at as many as 35 lakes with minimal sampling effort.Received March 16, 2015; accepted August 14, 2015

Neural Network: A better Approach for Software Effort Estimation

Software effort estimation is to find out the development effort required by a project. Before any project is being started firstly it is required to estimate the development effort required. This is one of the most important and challenging activities that has done before. Various techniques have been proposed to calculate effort estimation, but providing accurate cost is still an unachievable goal. This paper represents different neural network techniques that have been proposed to calculate effort estimation. It describes various techniques and approach proposed by different researchers representing how neural network gives accurate effort estimation in comparison with other techniques. This paper also focuses on how researchers have used various concepts of neural network and brought it into use to estimate effort.

Tuning of Use Case Point (UCP) Analysis Parameter using PSO

Test Effort Estimation is an important activity in software development. The test effort can be calculated on the basis of effort cost and time required for testing. Several studies have been done for developing test effort estimation models but to some extent only, most of these models result in erroneous results. So there is a strong need to optimize the efforts estimated. Meta heuristic techniques can be used for this purpose, to optimize a problem by iteratively trying to improve a solution, using some computational methods. In this paper, we have implemented meta-heuristic based search algorithm namely PSO. The particle swarm optimization algorithm is used for improving testing effort estimation. The particle swarm optimization algorithm (PSO) is applied on use case point (UCP) and results led us to the conclusion that test effort estimation can be optimized by applying PSO. The PSO optimization can also be applied for estimating efforts of software development. This implementation increases the accuracy of testing effort estimation.

Empirical Validation of Neural Network Models for Agile Software Effort Estimation based on Story Points

Abstract Now-a-days agile software development process has become famous in industries and substituting the traditional methods of software development. However, an accurate estimation of effort in this paradigm still remains a dispute in industries. Hence, the industry must be able to estimate the effort necessary for software development using agile methodology efficiently. For this, different techniques like expert opinion, analogy, disaggregation etc. are adopted by researchers and practitioners. But no proper mathematical model exists for this. The existing techniques are ad-hoc and are thus prone to be incorrect. One popular approach of calculating effort of agile projects mathematically is the Story Point Approach (SPA). In this study, an effort has been made to enhance the prediction accuracy of agile software effort estimation process using SPA. For doing this, different types of neural networks (General Regression Neural Network (GRNN), Probabilistic Neural Network (PNN), Group Method of Data Handling (GMDH) Polynomial Neural Network and Cascade-Correlation Neural Network) are used. Finally performance of the models generated using various neural networks are compared and analyzed.

Insightful analogy-based software development effort estimation through selective classification and localization

Accurate development effort estimation is a challenging issue in the management of software projects because it can considerably affect the planning and scheduling of a software project. Over the past few years, many algorithmic and non-algorithmic methods have been proposed to estimate the development effort in the early stages of project. Due to simplicity and estimation capability, analogy-based estimation (ABE) method has been widely accepted by researchers in this area. In spite of the fact that ABE is an efficient estimation method, it suffers from the non-normality and heterogeneous nature of software project datasets. Although prior studies have strived to remedy this issue by weighting, soft computing, and clustering techniques, the estimate accuracy is still not convincing and attempts are ongoing to reach more reliable estimates. The problem is that prior ABE-based studies have not considered the nature of software projects in the estimation process. This paper aims to show the effect of selective project classification and estimation process localization on the performance of ABE. An exhaustive investigation is conducted based on different development types, organization types, and development platforms as three underlying attributes in software projects. An evaluation framework is designed to reveal the ABE performance when it is combined with the proposed classification. A real dataset that includes 448 software projects is utilized for the evaluation purposes. The promising results showed that the estimate accuracy is significantly improved and the estimation process is considerably expedited if the nature of software projects is considered in the ABE method.

On the effectiveness of weighted moving windows: Experiment on linear regression based software effort estimation

AbstractIn construction of an effort estimation model, it seems effective to use a window of training data so that the model is trained with only recent projects. Considering the chronological order of projects within the window, and weighting projects according to their order within the window, may also affect estimation accuracy. In this study, we examined the effects of weighted moving windows on effort estimation accuracy. We compared weighted and non‐weighted moving windows under the same experimental settings. We confirmed that weighting methods significantly improved estimation accuracy in larger windows, although the methods also significantly worsened accuracy in smaller windows. This result contributes to understanding properties of moving windows. Copyright © 2014 John Wiley & Sons, Ltd.

Empirical Evaluation of machine learning techniques for software effort estimation

Accurate estimation of software development effort is a very difficult job.Both under estimation as well as over estimation can lead to serious consequences. So its very important to find a technique which can yield accurate results for software effort estimation. Here in our paper we have evaluated various machine learning techniques for software effort estimation like bagging, decision trees, decision tables, multilayer perceptron and RBF networks. Two different datasets i.e. heiatheiat dataset and miyazaki94 dataset have been used in our research. Decision trees outperform all other models in term of MMRE value. Results of machine