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Effort estimation is always quite a challenge, especially for agile software development projects. This paper describes the process of building a Bayesian network model for effort prediction in agile development. Very few studies have addressed the application of Bayesian networks to assess agile development efforts. Some research has not been validated in practice, and some has been validated on one or two projects. This paper aims to bring the implementation and use of Bayesian networks for effort prediction closer to the practitioners. This process consists of two phases. The Bayesian network model for task effort estimation is constructed and validated in the first phase on real agile projects. A relatively small model showed satisfactory estimation accuracy, but only five output intervals were used. The model was proven to be useful in daily work, but the project manager wanted to obtain more output intervals, although increasing the number of output intervals reduces the prediction accuracy. In the second phase, the focus is on increasing the number of output intervals while maintaining satisfactory accuracy. The advanced model for task effort estimation is developed and tested on real projects of two software firms. [ABSTRACT FROM AUTHOR]

Background: This paper investigates the impact of data quality on the performance of models predicting effort on software testing. Data quality was reflected by training data filtering strategies (data variants) covering combinations of Data Quality Rating, UFP Rating, and a threshold of valid cases. Methods: The experiment used the ISBSG dataset and 16 machine learning models. A process of three-fold cross-validation repeated 20 times was used to train and evaluate each model with each data variant. Model performance was assessed using absolute errors of prediction. A 'win–tie–loss' procedure, based on the Wilcoxon signed-rank test, was applied to identify the best models and data variants. Results: Most models, especially the most accurate, performed the best on a complete dataset, even though it contained cases with low data ratings. The detailed results include the rankings of the following: (1) models for particular data variants, (2) data variants for particular models, and (3) the best-performing combinations of models and data variants. Conclusions: Arbitrary and restrictive data selection to only projects with Data Quality Rating and UFP Rating of 'A' or 'B', commonly used in the literature, does not seem justified. It is recommended not to exclude cases with low data ratings to achieve better accuracy of most predictive models for testing effort prediction. [ABSTRACT FROM AUTHOR]

Effort estimation is always quite a challenge, especially for agile software development projects. This paper describes the process of building a Bayesian network model for effort prediction in agile development. Very few studies have addressed the application of Bayesian networks to assess agile development efforts. Some research has not been validated in practice, and some has been validated on one or two projects. This paper aims to bring the implementation and use of Bayesian networks for effort prediction closer to the practitioners. This process consists of two phases. The Bayesian network model for task effort estimation is constructed and validated in the first phase on real agile projects. A relatively small model showed satisfactory estimation accuracy, but only five output intervals were used. The model was proven to be useful in daily work, but the project manager wanted to obtain more output intervals, although increasing the number of output intervals reduces the prediction accuracy. In the second phase, the focus is on increasing the number of output intervals while maintaining satisfactory accuracy. The advanced model for task effort estimation is developed and tested on real projects of two software firms.

The maintenance of software‐intensive systems (SISs) must be undertaken to correct faults, improve the design, implement enhancements, adapt programmes such that different hardware, software, system features, and telecommunications facilities can be used, as well as to migrate legacy software. A lack of planning has been identified as one explanation for late and over budget software projects. An activity of planning is effort prediction. The goal of this study is to propose the application of a stochastic gradient boosting (SGB) model for predicting the SIS maintenance effort. We compare the SGB prediction accuracy with those obtained with statistical regression, neural network, support vector regression, decision trees, and association rules. We trained and tested the models with five SIS data sets selected from the International Software Benchmarking Standards Group Release 11. The SGB prediction accuracy was statistically better than the mentioned five models in the two larger data sets. We can conclude that a SGB can be applied to predict the maintenance effort of SISs coded in languages of the third generation and developed on either mainframes or multi‐platform. The predicted effort corresponds to the aggregate of efforts obtained from the project team, project management, and project administration. [ABSTRACT FROM AUTHOR]

To support agile software development projects, an array of tools and systems is available to plan, design, track, and manage the development process. In this paper, we explore a critical aspect of agile development i.e., effort prediction, that cuts across these tools and agile project teams. Accurate effort prediction can improve the planning of a sprint by enabling optimal assignments of both stories and developers. We develop a model for story-effort prediction using variables that are readily available when a story is created. We use seven predictive algorithms to predict a story's effort. Interestingly, none of the predictive algorithms consistently outperforms others in predicting story effort across our test data of 423 stories. We develop an ensemble-based method based on our model for predicting story effort. We conduct computational experiments to show that our ensemble-based approach performs better in comparison to other ensemble-based benchmarking approaches. We then demonstrate the practical application of our predictive model and our ensemble-based approach by optimizing sprint planning for two projects from our dataset using an optimization model. [ABSTRACT FROM AUTHOR]

Population models form the basis for the assessments of species in the tiger prawn component of Australia's northern prawn fishery. Penaeus semisulcatus and P. esculentus are assessed using a size-structured population model. These assessments form the basis for a control rule which predicts future total allowable catches (TACs) for P. semisulcatus and P. esculentus so that the discounted profit from the fishery is maximized. However, there are concerns with this approach: (i) the TAC predictions have consistently overpredicted actual catches and (ii) the assessment for one of the species exhibits a retrospective pattern. A series of analyses was conducted to explore the causes of these observations. Results indicate that catch, effort, and recruitment prediction can be improved substantially by changing the assumed selectivity pattern for one of the surveys, changing how the length frequency data are assembled from the raw data collected, changing the constraints on the minimum amount of effort by target fleet, modifying how the distribution of effort by week is forecasted, and dropping the length frequency data from the most recent recruitment survey. More generally, the analyses illustrate how retrospective analysis can be used to improve how assessments and projections are undertaken when the quantities of interest are known retrospectively. [ABSTRACT FROM PUBLISHER]

Cost overrun of software projects is major cause of their failures. In order to facilitate accurate software cost estimation, there are several metrics, tools and datasets. In this paper, we evaluate and compare different metrics and datasets in terms of similarities and differences of involved software attributes. These metrics forecast project cost estimations based on different software attributes. Some of these metrics are public and standard while others are only employed in a particular metric tool/dataset. Sixteen public cost estimation datasets are collected and analyzed. Different perspectives are used to compare and classify those datasets. Tools for feature selection and classification are used to find the most important attributes in cost estimation datasets toward the goal of effort prediction. In order to have better estimation, it is needed to correlate cost estimation from different resources, which requires a unified standard for software cost estimation metric tools and datasets. It is pertinent that a common cost estimation model may not work for each project due to diverse project size, application areas etc. We suggest having a standardized terminology of .project attributes used for cost estimation. This would improve cost estimation as multiple metrics could be applied on a project without much additional effort. [ABSTRACT FROM AUTHOR]

We have in previous studies reported our findings and concern about the reliability and validity of the evaluation procedures used in comparative studies on competing effort prediction models. In particular, we have raised concerns about the use of accuracy statistics to rank and select models. Our concern is strengthened by the observed lack of consistent findings. This study offers more insights into the causes of conclusion instability by elaborating on the findings of our previous work concerning the reliability and validity of the evaluation procedures. We show that model selection based on the accuracy statistics MMRE, MMER, MBRE, and MIBRE contribute to conclusion instability as well as selection of inferior models. We argue and show that the evaluation procedure must include an evaluation of whether the functional form of the prediction model makes sense to better prevent selection of inferior models. [ABSTRACT FROM AUTHOR]

Abstract: The inherent uncertainty of the software development process presents particular challenges for software effort prediction. We need to systematically address missing data values, outlier detection, feature subset selection and the continuous evolution of predictions as the project unfolds, and all of this in the context of data-starvation and noisy data. However, in this paper, we particularly focus on outlier detection, feature subset selection, and effort prediction at an early stage of a project. We propose a novel approach of using grey relational analysis (GRA) from grey system theory (GST), which is a recently developed system engineering theory based on the uncertainty of small samples. In this work we address some of the theoretical challenges in applying GRA to outlier detection, feature subset selection, and effort prediction, and then evaluate our approach on five publicly available industrial data sets using both stepwise regression and Analogy as benchmarks. The results are very encouraging in the sense of being comparable or better than other machine learning techniques and thus indicate that the method has considerable potential. [Copyright &y& Elsevier]

Accurate project effort prediction is an important goal for the software engineering community. To date most work has focused upon building algorithmic models of effort, for example COCOMO. These can be calibrated to local environments. We describe an alternative approach to estimation based upon the use of analogies. The underlying principle is to characterize projects in terms of features (for example, the number of interfaces, the development method or the size of the functional requirements document). Completed projects are stored and then the problem becomes one of finding the most similar projects to the one for which a prediction is required. Similarity is defined as Euclidean distance in n-dimensional space where n is the number of project features. Each dimension is standardized so all dimensions have equal weight. The known effort values of the nearest neighbors to the new project are then used as the basis for the prediction. The process is automated using a PC-based tool known as ANGEL. The method is validated on nine different industrial datasets (a total of 275 projects) and in all cases analogy outperforms algorithmic models based upon stepwise regression. From this work we argue that estimation by analogy is a viable technique that, at the very least, can be used by project managers to complement current estimation techniques. [ABSTRACT FROM AUTHOR]

Many studies have shown that important factors and key relationships often don't hold up well when transferred from one project to another. To deal with this seeming lack of global truisms in software engineering, it helps to develop a healthy skepticism and find ways to test our beliefs in key development practices against measures collected within the project context. [ABSTRACT FROM PUBLISHER]