Journal articles: 'Effort estimation'

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Capretz, Luiz Fernando, and Venus Marza. "Improving Effort Estimation by Voting Software Estimation Models." Advances in Software Engineering 2009 (September 1, 2009): 1–8. http://dx.doi.org/10.1155/2009/829725.

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Estimating software development effort is an important task in the management of large software projects. The task is challenging, and it has been receiving the attentions of researchers ever since software was developed for commercial purpose. A number of estimation models exist for effort prediction. However, there is a need for novel models to obtain more accurate estimations. The primary purpose of this study is to propose a precise method of estimation by selecting the most popular models in order to improve accuracy. Consequently, the final results are very precise and reliable when they are applied to a real dataset in a software project. Empirical validation of this approach uses the International Software Benchmarking Standards Group (ISBSG) Data Repository Version 10 to demonstrate the improvement in software estimation accuracy.

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Yücalar, Fatih, Deniz Kilinc, Emin Borandag, and Akin Ozcift. "Regression Analysis Based Software Effort Estimation Method." International Journal of Software Engineering and Knowledge Engineering 26, no. 05 (June 2016): 807–26. http://dx.doi.org/10.1142/s0218194016500261.

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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.

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Ayyıldız, Tülin Erçelebi, and Hasan Can Terzi. "Case Study on Software Effort Estimation." International Journal of Information and Electronics Engineering 7, no. 3 (May 2017): 103–7. http://dx.doi.org/10.18178/ijiee.2017.7.3.670.

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Şengüneş, Burcu, and Nursel Öztürk. "An Artificial Neural Network Model for Project Effort Estimation." Systems 11, no. 2 (February 9, 2023): 91. http://dx.doi.org/10.3390/systems11020091.

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Estimating the project effort remains a challenge for project managers and effort estimators. In the early phases of a project, having a high level of uncertainty and lack of experience cause poor estimation of the required work. Especially for projects that produce a highly customized unique product for each customer, it is challenging to make estimations. Project effort estimation has been studied mainly for software projects in the literature. Currently, there has been no study on estimating effort in customized machine development projects to the best of our knowledge. This study aims to fill this gap in the literature regarding project effort estimation for customized machine development projects. Additionally, this study focused on a single phase of a project, the automation phase, in which the machine is automated according to customer-specific requirements. Therefore, the effort estimation of this phase is crucial. In some cases, this is the first time that the company has experienced the requirements specific to the customer. For this purpose, this study proposed a model to estimate how much work is required to automate a machine. Insufficient effort estimation is one of the main reasons behind project failures, and nowadays, researchers prefer more objective approaches such as machine learning over expert-based ones. This study also proposed an artificial neural network (ANN) model for this purpose. Data from past projects were used to train the proposed ANN model. The proposed model was tested on 11 real-life projects and showed promising results with acceptable prediction accuracy. Additionally, a desktop application was developed to make this system easier to use for project managers.

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Pagadala, Srivyshnavi, Sony Bathala, and B. Uma. "An Efficient Predictive Paradigm for Software Reliability." Asian Journal of Computer Science and Technology 8, S3 (June 5, 2019): 114–16. http://dx.doi.org/10.51983/ajcst-2019.8.s3.2051.

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Software Estimation gives solution for complex problems in the software industry which gives estimates for cost and schedule. Software Estimation provides a comprehensive set of tips and heuristics that Software Developers, Technical Leads, and Project Managers can apply to create more accurate estimates. It presents key estimation strategies and addresses particular estimation challenges. In the planning of a software development project, a major challenge faced by project managers is to predict the defects and effort. The Software defect plays critical role in software product development. The estimation of defects can be determined in the product development using many advanced statistical modelling techniques based on the empirical data obtained by the testing phases. The proposed estimation technique in this paper is a model which was developed using Rayleigh function for estimating effect of defects in Software Project Management. The present study offers to decide how many defects creep in to production and determine the effort spent in months. The estimation model was used on Software Testing Life Cycle (STLC) to complete product. The accuracy of the model explains the variation in spent efforts in months associated with number of defects. The model helps the senior management in estimating the defects, schedule, cost and effort.

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Iwata, Kazunori, Toyoshiro Nakashima, Yoshiyuki Anan, and Naohiro Ishii. "Machine Learning Classification to Effort Estimation for Embedded Software Development Projects." International Journal of Software Innovation 5, no. 4 (October 2017): 19–32. http://dx.doi.org/10.4018/ijsi.2017100102.

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This paper discusses the effect of classification in estimating the amount of effort (in man-days) associated with code development. Estimating the effort requirements for new software projects is especially important. As outliers are harmful to the estimation, they are excluded from many estimation models. However, such outliers can be identified in practice once the projects are completed, and so they should not be excluded during the creation of models and when estimating the required effort. This paper presents classifications for embedded software development projects using an artificial neural network (ANN) and a support vector machine. After defining the classifications, effort estimation models are created for each class using linear regression, an ANN, and a form of support vector regression. Evaluation experiments are carried out to compare the estimation accuracy of the model both with and without the classifications using 10-fold cross-validation. In addition, the Games-Howell test with one-way analysis of variance is performed to consider statistically significant evidence.

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Puspaningrum, Alifia, Fachrul Pralienka Bani Muhammad, and Esti Mulyani. "Flower Pollination Algorithm for Software Effort Coefficients Optimization to Improve Effort Estimation Accuracy." JUITA: Jurnal Informatika 9, no. 2 (November 30, 2021): 139. http://dx.doi.org/10.30595/juita.v9i2.10511.

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Software effort estimation is one of important area in project management which used to predict effort for each person to develop an application. Besides, Constructive Cost Model (COCOMO) II is a common model used to estimate effort estimation. There are two coefficients in estimating effort of COCOMO II which highly affect the estimation accuracy. Several methods have been conducted to estimate those coefficients which can predict a closer value between actual effort and predicted value. In this paper, a new metaheuristic algorithm which is known as Flower Pollination Algorithm (FPA) is proposed in several scenario of iteration. Besides, FPA is also compared to several metaheuristic algorithm, namely Cuckoo Search Algorithm and Particle Swarm Optimization. After evaluated by using Mean Magnitude of Relative Error (MMRE), experimental results show that FPA obtains the best result in estimating effort compared to other algorithms by reached 52.48% of MMRE in 500 iterations.

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Deng, Jeremiah D., Martin Purvis, and Maryam Purvis. "Software Effort Estimation." International Journal of Intelligent Information Technologies 7, no. 3 (July 2011): 41–53. http://dx.doi.org/10.4018/jiit.2011070104.

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Software development effort estimation is important for quality management in the software development industry, yet its automation still remains a challenging issue. Applying machine learning algorithms alone often cannot achieve satisfactory results. This paper presents an integrated data mining framework that incorporates domain knowledge into a series of data analysis and modeling processes, including visualization, feature selection, and model validation. An empirical study on the software effort estimation problem using a benchmark dataset shows the necessity and effectiveness of the proposed approach.

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Basten, Dirk, and Thomas Hoerstrup. "Organizational Effort Estimation." Computer 47, no. 8 (August 2014): 76–79. http://dx.doi.org/10.1109/mc.2014.216.

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Ludwig, D., and C. J. Walters. "A Robust Method for Parameter Estimation from Catch and Effort Data." Canadian Journal of Fisheries and Aquatic Sciences 46, no. 1 (January 1, 1989): 137–44. http://dx.doi.org/10.1139/f89-018.

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The problem of robust estimation of optimal effort levels from surplus production models is considered. A variety of models are used to generate data, for the purpose of testing estimation schemes. The result of an estimation is an estimate of the optimal effort. These efforts are compared using the expected discounted value of a deterministic stock, which corresponds to the model used to generate the data. Such a criterion takes into account not only the loss due to bias in the estimated optimal effort, but also the loss due to the variance of the estimator. Estimation is difficult if there is a lack of informative variation in effort levels or stock sizes. In such cases, the estimation scheme which maximizes the criterion described above sacrifices realism in the representation of the stock-production relationship in order to reduce the variance of the estimate of optimal effort. We present a composite estimation scheme which performs acceptably in all the cases we have examined, and whose performance degrades slowly as the amount of information in the data decreases.

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Mukunga, Catherine Wambui, John Gichuki Ndia, and Geoffrey Mariga Wambugu. "A METRICS -BASED MODEL FOR ESTIMATING THE MAINTENANCE EFFORT OF PYTHON SOFTWARE." International Journal of Software Engineering & Applications 14, no. 3 (May 26, 2023): 15–29. http://dx.doi.org/10.5121/ijsea.2023.14302.

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Software project management includes a substantial area for estimating software maintenance effort. Estimation of software maintenance effort improves the overall performance and efficiency of software. The Constructive Cost Model (COCOMO) and other effort estimation models are mentioned in literature but are inappropriate for Python programming language. This research aimed to modify the Constructive Cost Model (COCOMO II) by considering a range of Python maintenance effort influencing factors to get estimations and incorporated size and complexity metrics to estimate maintenance effort. A within-subjects experimental design was adopted and an experiment questionnaire was administered to forty subjects aiming to rate the maintainability of twenty Python programs. Data collected from the experiment questionnaire was analyzed using descriptive statistics. Metric values were collected using a developed metric tool. The subject ratings on software maintainability were correlated with the developed model’s maintenance effort, a strong correlation of 0.610 was reported meaning that the model is valid.

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Latif, Abdul, Lady Agustin Fitriana, and Muhammad Rifqi Firdaus. "COMPARATIVE ANALYSIS OF SOFTWARE EFFORT ESTIMATION USING DATA MINING TECHNIQUE AND FEATURE SELECTION." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 6, no. 2 (February 2, 2021): 167–74. http://dx.doi.org/10.33480/jitk.v6i2.1968.

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Software development involves several interrelated factors that influence development efforts and productivity. Improving the estimation techniques available to project managers will facilitate more effective time and budget control in software development. Software Effort Estimation or software cost/effort estimation can help a software development company to overcome difficulties experienced in estimating software development efforts. This study aims to compare the Machine Learning method of Linear Regression (LR), Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Decision Tree Random Forest (DTRF) to calculate estimated cost/effort software. Then these five approaches will be tested on a dataset of software development projects as many as 10 dataset projects. So that it can produce new knowledge about what machine learning and non-machine learning methods are the most accurate for estimating software business. As well as knowing between the selection between using Particle Swarm Optimization (PSO) for attributes selection and without PSO, which one can increase the accuracy for software business estimation. The data mining algorithm used to calculate the most optimal software effort estimate is the Linear Regression algorithm with an average RMSE value of 1603,024 for the 10 datasets tested. Then using the PSO feature selection can increase the accuracy or reduce the RMSE average value to 1552,999. The result indicates that, compared with the original regression linear model, the accuracy or error rate of software effort estimation has increased by 3.12% by applying PSO feature selection

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Valdés-Souto, Francisco, and Lizbeth Naranjo-Albarrán. "Software project estimation using smooth curve methods and variable selection and regularization methods using a wedge-shape form database." Proceedings of the Institute for System Programming of the RAS 35, no. 1 (2023): 123–40. http://dx.doi.org/10.15514/ispras-2023-35(1)-9.

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Context: The impact of an excellent estimation in planning, budgeting, and control, makes the estimation activities an essential element for the software project success. Several estimation techniques have been developed during the last seven decades. Traditional regression-based is the most often estimation method used in the literature. The generation of models needs a reference database, which is usually a wedge-shaped dataset when real projects are considered. The use of regression-based estimation techniques provides low accuracy with this type of database. Objective: Evaluate and provide an alternative to the general practice of using regression-based models, looking if smooth curve methods and variable selection and regularization methods provide better reliability of the estimations based on the wedge-shaped form databases. Method: A previous study used a reference database with a wedge-shaped form to build a regression-based estimating model. This paper utilizes smooth curve methods and variable selection and regularization methods to build estimation models, providing an alternative to linear regression models. Results: The results show the improvement in the estimation results when smooth curve methods and variable selection and regularization methods are used against regression-based models when wedge-shaped form databases are considered. For example, GAM with all the variables show that the R-squared is for Effort: 0.6864 and for Cost: 0.7581; the MMRE is for Effort: 0.1095 and for Cost: 0.0578. The results for the GAM with LASSO show that the R-squared is for Effort: 0.6836 and for Cost: 0.7519; the MMRE is for Effort: 0.1105 and for Cost: 0.0585. In comparison to the R-squared is for Effort: 0.6790 and for Cost: 0.7540; the MMRE is for Effort: 0.1107 and for Cost: 0.0582 while using MLR.

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Gould, W. R., L. A. Stefanski, and K. H. Pollock. "Use of simulation–extrapolation estimation in catch–effort analyses." Canadian Journal of Fisheries and Aquatic Sciences 56, no. 7 (July 1, 1999): 1234–40. http://dx.doi.org/10.1139/f99-052.

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All catch-effort estimation methods implicitly assume catch and effort are known quantities, whereas in many cases, they have been estimated and are subject to error. We evaluate the application of a simulation-based estimation procedure for measurement error models (J.R. Cook and L.A. Stefanski. 1994. J. Am. Stat. Assoc. 89: 1314-1328) in catch-effort studies. The technique involves a simulation component and an extrapolation step, hence the name SIMEX estimation. We describe SIMEX estimation in general terms and illustrate its use with applications to real and simulated catch and effort data. Correcting for measurement error with SIMEX estimation resulted in population size and catchability coefficient estimates that were substantially less than naive estimates, which ignored measurement errors in some cases. In a simulation of the procedure, we compared estimators from SIMEX with "naive" estimators that ignore measurement errors in catch and effort to determine the ability of SIMEX to produce bias-corrected estimates. The SIMEX estimators were less biased than the naive estimators but in some cases were also more variable. Despite the bias reduction, the SIMEX estimator had a larger mean squared error than the naive estimator for one of two artificial populations studied. However, our results suggest the SIMEX estimator may outperform the naive estimator in terms of bias and precision for larger populations.

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WALTHER, B. A., and S. MORAND. "Comparative performance of species richness estimation methods." Parasitology 116, no. 4 (April 1998): 395–405. http://dx.doi.org/10.1017/s0031182097002230.

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In most real-world contexts the sampling effort needed to attain an accurate estimate of total species richness is excessive. Therefore, methods to estimate total species richness from incomplete collections need to be developed and tested. Using real and computer-simulated parasite data sets, the performances of 9 species richness estimation methods were compared. For all data sets, each estimation method was used to calculate the projected species richness at increasing levels of sampling effort. The performance of each method was evaluated by calculating the bias and precision of its estimates against the known total species richness. Performance was evaluated with increasing sampling effort and across different model communities. For the real data sets, the Chao2 and first-order jackknife estimators performed best. For the simulated data sets, the first-order jackknife estimator performed best at low sampling effort but, with increasing sampling effort, the bootstrap estimator outperformed all other estimators. Estimator performance increased with increasing species richness, aggregation level of individuals among samples and overall population size. Overall, the Chao2 and the first-order jackknife estimation methods performed best and should be used to control for the confounding effects of sampling effort in studies of parasite species richness. Potential uses of and practical problems with species richness estimation methods are discussed.

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Resmi, V., and S. Vijayalakshmi. "Analogy-Based Approaches to Improve Software Project Effort Estimation Accuracy." Journal of Intelligent Systems 29, no. 1 (June 27, 2019): 1468–79. http://dx.doi.org/10.1515/jisys-2019-0023.

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Abstract In the discipline of software development, effort estimation renders a pivotal role. For the successful development of the project, an unambiguous estimation is necessitated. But there is the inadequacy of standard methods for estimating an effort which is applicable to all projects. Hence, to procure the best way of estimating the effort becomes an indispensable need of the project manager. Mathematical models are only mediocre in performing accurate estimation. On that account, we opt for analogy-based effort estimation by means of some soft computing techniques which rely on historical effort estimation data of the successfully completed projects to estimate the effort. So in a thorough study to improve the accuracy, models are generated for the clusters of the datasets with the confidence that data within the cluster have similar properties. This paper aims mainly on the analysis of some of the techniques to improve the effort prediction accuracy. Here the research starts with analyzing the correlation coefficient of the selected datasets. Then the process moves through the analysis of classification accuracy, clustering accuracy, mean magnitude of relative error and prediction accuracy based on some machine learning methods. Finally, a bio-inspired firefly algorithm with fuzzy analogy is applied on the datasets to produce good estimation accuracy.

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Chowdary, E. J. Sai Pavan. "Software Effort Estimation Techniques." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (April 30, 2018): 2497–500. http://dx.doi.org/10.22214/ijraset.2018.4424.

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Kumar, Neeraj, Yogesh Kumar, and Rahul Rishi. "Software Effort Estimation Techniques." International Journal of Computer Sciences and Engineering 7, no. 1 (January 31, 2019): 139–42. http://dx.doi.org/10.26438/ijcse/v7i1.139142.

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Kushwaha, Dharmender Singh, and A. K. Misra. "Software test effort estimation." ACM SIGSOFT Software Engineering Notes 33, no. 3 (May 2008): 1–5. http://dx.doi.org/10.1145/1360602.1361211.

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Shivakumar, Shailesh Kumar. "Software Estimation Framework for Packaged Products." International Journal of Project Management and Productivity Assessment 9, no. 1 (January 2021): 15–24. http://dx.doi.org/10.4018/ijpmpa.2021010102.

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Packaged products play a major role in successful implementation of various software projects. Many of the software solutions are built around packaged products. In this paper, the authors propose a novel “software packaged product estimation framework” for an end to end estimation framework for estimating effort for packaged products. The software packaged product estimation framework provides end to end estimation coverage for various project lifecycle stages and supporting activities. The software packaged product estimation framework was used to predict the effort for two projects with MMRE of 0.261 and pred(0.3) of 66.67%.

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Bosu, Michael Franklin, Stephen G. MacDonell, and Peter A. Whigham. "Analyzing the Stationarity Process in Software Effort Estimation Datasets." International Journal of Software Engineering and Knowledge Engineering 30, no. 11n12 (November 2020): 1607–40. http://dx.doi.org/10.1142/s0218194020400239.

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Software effort estimation models are typically developed based on an underlying assumption that all data points are equally relevant to the prediction of effort for future projects. The dynamic nature of several aspects of the software engineering process could mean that this assumption does not hold in at least some cases. This study employs three kernel estimator functions to test the stationarity assumption in five software engineering datasets that have been used in the construction of software effort estimation models. The kernel estimators are used in the generation of nonuniform weights which are subsequently employed in weighted linear regression modeling. In each model, older projects are assigned smaller weights while the more recently completed projects are assigned larger weights, to reflect their potentially greater relevance to present or future projects that need to be estimated. Prediction errors are compared to those obtained from uniform models. Our results indicate that, for the datasets that exhibit underlying nonstationary processes, uniform models are more accurate than the nonuniform models; that is, models based on kernel estimator functions are worse than the models where no weighting was applied. In contrast, the accuracies of uniform and nonuniform models for datasets that exhibited stationary processes were essentially equivalent. Our analysis indicates that as the heterogeneity of a dataset increases, the effect of stationarity is overridden. The results of our study also confirm prior findings that the accuracy of effort estimation models is independent of the type of kernel estimator function used in model development.

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AMAZAL, FATIMA AZZAHRA, ALI IDRI, and ALAIN ABRAN. "SOFTWARE DEVELOPMENT EFFORT ESTIMATION USING CLASSICAL AND FUZZY ANALOGY: A CROSS-VALIDATION COMPARATIVE STUDY." International Journal of Computational Intelligence and Applications 13, no. 03 (September 2014): 1450013. http://dx.doi.org/10.1142/s1469026814500138.

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Software effort estimation is one of the most important tasks in software project management. Of several techniques suggested for estimating software development effort, the analogy-based reasoning, or Case-Based Reasoning (CBR), approaches stand out as promising techniques. In this paper, the benefits of using linguistic rather than numerical values in the analogy process for software effort estimation are investigated. The performance, in terms of accuracy and tolerance of imprecision, of two analogy-based software effort estimation models (Classical Analogy and Fuzzy Analogy, which use numerical and linguistic values respectively to describe software projects) is compared. Three research questions related to the performance of these two models are discussed and answered. This study uses the International Software Benchmarking Standards Group (ISBSG) dataset and confirms the usefulness of using linguistic instead of numerical values in analogy-based software effort estimation models.

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Gultekin, Muaz, and Oya Kalipsiz. "Story Point-Based Effort Estimation Model with Machine Learning Techniques." International Journal of Software Engineering and Knowledge Engineering 30, no. 01 (January 2020): 43–66. http://dx.doi.org/10.1142/s0218194020500035.

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Until now, numerous effort estimation models for software projects have been developed, most of them producing accurate results but not providing the flexibility to decision makers during the software development process. The main objective of this study is to objectively and accurately estimate the effort when using the Scrum methodology. A dynamic effort estimation model is developed by using regression-based machine learning algorithms. Story point as a unit of measure is used for estimating the effort involved in an issue. Projects are divided into phases and the phases are respectively divided into iterations and issues. Effort estimation is performed for each issue, then the total effort is calculated with aggregate functions respectively for iteration, phase and project. This architecture of our model provides flexibility to decision makers in any case of deviation from the project plan. An empirical evaluation demonstrates that the error rate of our story point-based estimation model is better than others.

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Gould, W. R., and K. H. Pollock. "Catch-effort maximum likelihood estimation of important population parameters." Canadian Journal of Fisheries and Aquatic Sciences 54, no. 4 (April 1, 1997): 890–97. http://dx.doi.org/10.1139/f96-327.

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The relative ease with which linear regression models are understood explains the popularity of such techniques in estimating population size with catch-effort data. However, the development and use of the regression models require assumptions and approximations that may not accurately reflect reality. We present the model development necessary for maximum likelihood estimation of parameters from catch-effort data using the program SURVIV, the primary intent being to present biologists with a vehicle for producing maximum likelihood estimates in lieu of using the traditional regression techniques. The differences between the regression approaches and maximum likelihood estimation will be illustrated with an example of commercial fishery catch-effort data and through simulation. Our results indicate that maximum likelihood estimation consistently provides less biased and more precise estimates than the regression methods and allows for greater model flexibility necessary in many circumstances. We recommend the use of maximum likelihood estimation in future catch-effort studies.

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M Kirmani, Mudasir. "Re-UCP Effort Estimation for Web Application Development." Oriental journal of computer science and technology 10, no. 04 (December 28, 2017): 755–59. http://dx.doi.org/10.13005/ojcst/10.04.08.

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Practical effort estimation is very basic and most essential aspect of very organisation to sustain better project management. Accuracy in efforts makes the organisation to surge its business and build motivation among its customers effectively. It was observed that continuous improvement is required for software effort assessment after regular eras.The fundamental point of this examination work is to assess the execution of Re-UCP strategy for estimation of efforts for web application projects. This research work compares the existing effort estimation model results with the Re-UCP effort estimation methods results for web application development projects.

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Gharehchopogh, Farhad Soleimanian, Isa Maleki, and Seyyed Reza Khaze. "A novel particle swarm optimization approach for software effort estimation." International Journal of Academic Research 6, no. 2 (March 30, 2014): 69–76. http://dx.doi.org/10.7813/2075-4124.2014/6-2/a.12.

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Abdulmehdi, Ziyad T., M. S. Saleem Basha, Mohamed Jameel, and P. Dhavachelvan. "A Variant of COCOMO II for Improved Software Effort Estimation." International Journal of Computer and Electrical Engineering 6, no. 4 (2014): 346–50. http://dx.doi.org/10.7763/ijcee.2014.v6.851.

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Silhavy, Petr, Radek Silhavy, and Zdenka Prokopova. "Spectral Clustering Effect in Software Development Effort Estimation." Symmetry 13, no. 11 (November 8, 2021): 2119. http://dx.doi.org/10.3390/sym13112119.

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Software development effort estimation is essential for software project planning and management. In this study, we present a spectral clustering algorithm based on symmetric matrixes as an option for data processing. It is expected that constructing an estimation model on more similar data can increase the estimation accuracy. The research methods employ symmetrical data processing and experimentation. Four experimental models based on function point analysis, stepwise regression, spectral clustering, and categorical variables have been conducted. The results indicate that the most advantageous variant is a combination of stepwise regression and spectral clustering. The proposed method provides the most accurate estimates compared to the baseline method and other tested variants.

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A, Venkatesh, Pavan ., Santosh ., Yugandhar G, and Sunil Manoli. "Software Effort Estimation Based on Use Case Reuse (Back Propagation)." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 2858–66. http://dx.doi.org/10.22214/ijraset.2023.50822.

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Abstract: Project managers often use effort estimating strategies to manage the human resources of current or upcoming software projects. Prior to project implementation, cost, time, andpersonnel estimation are basically necessary. For every project of software, getting accuracy in Effort Estimation has always been difficult. In this study, the estimation of software development effort was determined using a back propagation model. This model's goal is to investigate the capabilities and potential uses of Utilizing artificial neural networks (ANN) as a tool for forecasting the effort required for software development. In order to estimate the software work, we are attempting to implement a machine learning technique in this research. Out of all machine learning methods, we are applying an algorithm based on Artificial Neural Networks that is Back propagation. The Desharnais dataset, a well-known publicly available dataset for estimation of software effort, is used to test the approach. The performance and accuracy of the tested model have been evaluated using three metrics: MMRE, MRE, and Pred (0.25). In the sections below that follows, I explain the algorithm and its results

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HAAPIO, TOPI, and TIM MENZIES. "EXPLORING THE EFFORT OF GENERAL SOFTWARE PROJECT ACTIVITIES WITH DATA MINING." International Journal of Software Engineering and Knowledge Engineering 21, no. 05 (September 2011): 725–53. http://dx.doi.org/10.1142/s0218194011005438.

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Software project effort estimation requires high accuracy, but accurate estimations are difficult to achieve. Increasingly, data mining is used to improve an organization's software process quality, e.g. the accuracy of effort estimations. Data is collected from projects, and data miners are used to discover beneficial knowledge. This paper reports a data mining experiment in which we examined 32 software projects to improve effort estimation. We examined three major categories of software project activities, and focused on the activities of the category which has got the least attention in research so far, the non-construction activities. The analysis is based on real software project data supplied by a large European software company. In our data mining experiment, we applied a range of machine learners. We found that the estimated total software project effort is a predictor in modeling and predicting the actual quality management effort of the project.

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Bravo-Estrada, Diego, and Roxana López-Cruz. "Breve revisión de los modelos clásicos de Estimación de Esfuerzo para proyectos de desarrollo de Software." Selecciones Matemáticas 10, no. 01 (July 26, 2023): 199–209. http://dx.doi.org/10.17268/sel.mat.2023.01.17.

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A critical synthesis on the most representative models for software development project effort estimation is provided. This work is a basis for a discussion about the methodological and practical challenges which entail the effort estimation field, specially in the mathematical/statistical modelling fundamentals, and its empirical verification in the software industry.

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Iok Kuan, Simon WU. "Factors on Software Effort Estimation." International Journal of Software Engineering & Applications 8, no. 1 (January 30, 2017): 23–32. http://dx.doi.org/10.5121/ijsea.2017.8103.

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Drews, Christopher, and Birger Lantow. "Effort Estimation in BPMS Migration." Complex Systems Informatics and Modeling Quarterly, no. 14 (April 30, 2018): 38–53. http://dx.doi.org/10.7250/csimq.2018-14.03.

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Chandra, Vishal, and Savita Shiwani. "Fuzzy based Effort Estimation Approach." International Journal of Computer Applications 103, no. 17 (October 18, 2014): 39–42. http://dx.doi.org/10.5120/18305-9438.

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Chandani, Priyanka, and Chetna Gupta. "Towards Risk Based Effort Estimation." International Journal of Information System Modeling and Design 9, no. 4 (October 2018): 54–71. http://dx.doi.org/10.4018/ijismd.2018100104.

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Power consumption mainly takes place in three stages: processing the data, receiving the data, and transmitting the data. Power consumption in data transmitting is one of the most important phenomena in wireless sensor networks (WSNs). In this article, authors analyze the power transmission in three scenarios with 100 and 500 nodes for 100 and 1000 sq. meters of area respectively and design a network which should be more efficient in power saving. Results analysis section presents different data aggregation techniques and their impact on the power transmission in WSNs. Three different scenarios have been used during simulation of network in Matlab. After that, the authors find that the proposed approach has outperformed in the first two scenarios. However, in the third scenario, results are partially better as compared to the existing approaches (tree-based, cluster-based, chain-based, and grid-based). The proposed approach, PLBDA, is 10.30%, 18.55%, 37.11%, and 55.67% better for transmission power save in comparison to tree-based, cluster-based, grid-based, and chain-based approaches respectively.

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Abraham, Cerene Mariam, M. Sudheep Elayidom, and T. Santhanakrishnan. "Towards Risk Based Effort Estimation." International Journal of Information System Modeling and Design 9, no. 4 (October 2018): 67–84. http://dx.doi.org/10.4018/ijismd.2018100105.

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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.

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Höst, Martin, and Claes Wohlin. "A subjective effort estimation experiment." Information and Software Technology 39, no. 11 (1997): 755–62. http://dx.doi.org/10.1016/s0950-5849(97)00027-x.

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Ohlsson, M. C., C. Wohlin, and B. Regnell. "A project effort estimation study." Information and Software Technology 40, no. 14 (December 1998): 831–39. http://dx.doi.org/10.1016/s0950-5849(98)00097-4.

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Kocaguneli, Ekrem, Tim Menzies, and Emilia Mendes. "Transfer learning in effort estimation." Empirical Software Engineering 20, no. 3 (March 29, 2014): 813–43. http://dx.doi.org/10.1007/s10664-014-9300-5.

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Heidrich, Jens, Markku Oivo, and Andreas Jedlitschka. "Software productivity and effort estimation." Journal of Software: Evolution and Process 27, no. 7 (June 26, 2015): 465–66. http://dx.doi.org/10.1002/smr.1722.

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Kocaguneli, Ekrem, Tim Menzies, Jacky Keung, David Cok, and Ray Madachy. "Active learning and effort estimation: Finding the essential content of software effort estimation data." IEEE Transactions on Software Engineering 39, no. 8 (August 2013): 1040–53. http://dx.doi.org/10.1109/tse.2012.88.

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Kondo, Masanari, Osamu Mizuno, and Eun-Hye Choi. "Causal-Effect Analysis using Bayesian LiNGAM Comparing with Correlation Analysis in Function Point Metrics and Effort." International Journal of Mathematical, Engineering and Management Sciences 3, no. 2 (June 1, 2018): 90–112. http://dx.doi.org/10.33889/ijmems.2018.3.2-008.

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Software effort estimation is a critical task for successful software development, which is necessary for appropriately managing software task assignment and schedule and consequently producing high quality software. Function Point (FP) metrics are commonly used for software effort estimation. To build a good effort estimation model, independent explanatory variables corresponding to FP metrics are required to avoid a multicollinearity problem. For this reason, previous studies have tackled analyzing correlation relationships between FP metrics. However, previous results on the relationships have some inconsistencies. To obtain evidences for such inconsistent results and achieve more effective effort estimation, we propose a novel analysis, which investigates causal-effect relationships between FP metrics and effort. We use an advanced linear non-Gaussian acyclic model called BayesLiNGAM for our causal-effect analysis, and compare the correlation relationships with the causal-effect relationships between FP metrics. In this paper, we report several new findings including the most effective FP metric for effort estimation investigated by our analysis using two datasets.

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Lalitha, R., B. Latha, and G. Sumathi. "Use case repository framework based on machine learning algorithm to analyze the software development estimation with intelligent information systems." International Journal of Wavelets, Multiresolution and Information Processing 18, no. 01 (March 27, 2019): 1941007. http://dx.doi.org/10.1142/s0219691319410078.

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The success of information system process depends on accuracy of software estimation. Estimation is done at initial phase of software development. It requires a collection of all relevant required information for estimating the software effort. In this paper, a methodology is proposed to maintain a knowledgeable use case repository to store the use cases of various projects in several software project-related domains. This acts as a reference model to compare similar use cases of similar types of projects. The use case points are calculated and using this, schedule estimation and effort estimation of a project are calculated using the formulas of software engineering. These values are compared with the estimated effort and scheduled effort of a new project under development. Apart from these, the effective machine learning technique called neural network is used to measure how accurately the information is processed by use of case repository framework. The proposed machine learning-based use case repository system helps to estimate and analyze the effort using the machine learning algorithms.

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Cheng, Bo, and Xue Jun Yu. "The Selection of Agile Development's Effort Estimation Factors Based on Principal Component Analysis." Advanced Materials Research 748 (August 2013): 1229–34. http://dx.doi.org/10.4028/www.scientific.net/amr.748.1229.

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Software development managers could improve the quality of software products through controlling the development time and budget in software development process by using software effort estimation. But until now, there have not effective methods estimating effort for agile development. In this paper, the author extracts agile development data from thousands of projects data provided by ISBSG DATA Release 11, and analyze agile development data using the method of principal component analysis. Finally, the paper gets out the set of agile development factor affecting effort estimation.

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ZAPATA, A. H., and M. R. V. CHAUDRON. "AN EMPIRICAL STUDY INTO THE ACCURACY OF IT ESTIMATIONS AND ITS INFLUENCING FACTORS." International Journal of Software Engineering and Knowledge Engineering 23, no. 04 (May 2013): 409–32. http://dx.doi.org/10.1142/s0218194013400081.

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This paper is the result of two related studies done on the estimation of IT projects at a large Dutch multinational company. The first one is a study about the accuracy of different dimensions of IT project estimating: schedule, budget and effort. [Note: This paper is an extension of the paper published by the authors as "An analysis of accuracy and learning in software project estimating" [28].] This study is based on a dataset of 171 projects collected at the IT department of the company. We analyzed the estimation error of budget, effort and schedule. Also, we analyzed whether there is any learning (improvement) effect over time. With the results of the first study we proceeded to research what is causing the current estimation error (inaccuracy). The results of our first study show that there is no relation between accuracy of budget, schedule and effort in the company analyzed. Besides, they show that over time there is no change in the inaccuracy (effectiveness and efficiency of the estimates). In our second study we discovered that the sources of this inaccuracy are: (IT estimation) process complexity, misuse of estimates, technical complexity, requirements redefinition and business domain instability. This paper reflects and provides recommendations on how to improve the learning from historical estimates and how to manage the diverse sources of inaccuracy inside this particular company and also in other organizations.

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Stankovski, Dragan. "Measurements that matter with hybrid model of estimation." Journal of CIEES 1, no. 2 (December 22, 2021): 35–39. http://dx.doi.org/10.48149/jciees.2021.1.2.7.

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The biggest challenge of delivering projects on time, especially in the field of Telecommunication, is proper planning and correct estimation of the effort. Such estimations while different phases of the development could be extremely thorny and easily can jeopardize final goals and handover on time. Thus, in this paper, the proposed approach of Hybrid model estimation is to reduce such unlike stakes and give a clear definition of “how needs to be done” with regular estimations transferred to Story Points.

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KaurSehra, Sumeet, Jasneet Kaur, and Sukhjit Singh Sehra. "Effect of Data Preprocessing on Software Effort Estimation." International Journal of Computer Applications 69, no. 25 (May 17, 2013): 29–32. http://dx.doi.org/10.5120/12130-8506.

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Sharma, Amrita, and Neha Chaudhary. "The combined model for software development effort estimation using polynomial regression for heterogeneous projects." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 2 (May 18, 2022): 75–82. http://dx.doi.org/10.32620/reks.2022.2.06.

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Subject matter: Estimating the software work is a crucial job of persons participating in software project management. The difficulty in predicting effort is compounded by the fact that software development is always changing. In the past, researchers used one form of development methodology in their work to estimate effort and time. Estimations of the software projects are estimated with different size matrices. The lines of code, story point and use case point are required for the estimation using algorithmic models for procedural, agile, and object-oriented development approaches. Currently, the companies use these three types of size matrices for estimating projects. Not any one model present estimates the effort for different development approaches with different size metrics. This paper proposes a combined software estimation model for three types of development methodologies with regression analysis. The estimation can be done with the proposed model for a software project developed using the procedural, agile, and object-oriented approach. Method: The input for the model is the size of the software, such as lines of code, story point, and use case point. The model is developed using the polynomial regression. The model is developed with the four constant parameters that are based on the procedural, agile, and object-oriented projects. A dataset of python projects for procedural, zia dataset for agile, company dataset for object-oriented methodology is used to propose the model. Conclusion: The effort is predicted for the procedural, agile, and object-oriented projects with the polynomial regression model and compare the results to existing models to validate the work. The R2 is used to measure accuracy and the MMRE is used to determine error. The accuracy of the proposed model was higher than 90% and the error was found to be less than 0.05. The results are compared with case-based reasoning and an ensemble model for the procedural approach, linear regression and Bayesian network for the agile approach, and linear and log-linear regression for object-oriented approach. The minimum error and maximum accuracy is achieved compared to these techniques.

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Aggarwal, K. K., Yogesh Singh, and Jitender Kumar Chhabra. "F-effort: a fuzzified model of software effort estimation." Journal of Discrete Mathematical Sciences and Cryptography 7, no. 3 (January 2004): 387–400. http://dx.doi.org/10.1080/09720529.2004.10698016.

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Prykhodko, Sergiy, Ivan Shutko, and Andrii Prykhodko. "Early size estimation of web apps created using codeigniter framework by nonlinear regression models." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 3 (October 4, 2022): 84–94. http://dx.doi.org/10.32620/reks.2022.3.06.

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Subject matter: Early software size estimation is one of the project managers' significant problems in evaluating app development efforts because software size is the major determinant of software project effort. Function points (FPs) and lines of code (LOC) are most commonly used as measures of size in existing software effort estimation methods and models. As is known, both these metrics have their advantages and disadvantages when used for software effort estimation. Although the FPs-based measure has the advantage over the LOC in that it does not depend on the technologies used, however, the assessment of efforts requires considering such factors (environmental factors). Considering the above factors can be ensured by appropriate models for estimating the LOC-based effort. Nowadays, many Web apps are created using PHP frameworks making the app development faster. CodeIgniter is one such powerful framework. However, there are no regression models for estimating the software size of Web apps created using the CodeIgniter framework. This requires the construction of the appropriate models. The task of this paper is to develop a nonlinear regression model for estimating the software size (in KLOC, kilo lines of code) of Web apps created using the CodeIgniter framework. Method: We apply the technique for constructing nonlinear regression models based on the multivariate normalizing transformations and prediction intervals. The result is three nonlinear regression models with three predictors: the total number of classes, the average number of methods per class, and the DIT (Depth of Inheritance Tree) average per class. To build these models for estimating the size of Web apps created using the CodeIgniter framework, we used three well-known normalizing transformations: two univariate transformations (the decimal logarithm and the Box-Cox transformation) and the Box-Cox four-variate transformation. Conclusions. The nonlinear regression model constructed by the Box-Cox four-variate transformation has better size prediction results than other regression models based on the univariate transformations.

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Journal articles on the topic 'Effort estimation'

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