A Baseline Model for Software Effort Estimation

Whigham, Peter A.; Owen, Caitlin A.; MacDonell, Stephen G.

doi:10.1145/2738037

Cited by 86 publications

(114 citation statements)

References 47 publications

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“…More recently, Whigham et al found that claims made in two recent cost estimation studies could not be confirmed by independent analyses [47]. RR emphasizes the need to specify, fully, any statistical analysis, in order to address problems such as these.…”

Section: Problems With Empirical Software Engineering Practicementioning

confidence: 99%

Would wider adoption of reproducible research be beneficial for empirical software engineering research?

Madeyski

Kitchenham

2017

IFS

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Abstract.Researchers have identified problems with the validity of software engineering research findings. In particular, it is often impossible to reproduce data analyses, due to lack of raw data, or sufficient summary statistics, or undefined analysis procedures. The aim of this paper is to raise awareness of the problems caused by unreproducible research in software engineering and to discuss the concept of reproducible research (RR) as a mechanism to address these problems. RR is the idea that the outcome of research is both a paper and its computational environment. We report some recent studies that have cast doubts on the reliability of research outcomes in software engineering. Then we discuss the use of RR as a means of addressing these problems. We discuss the use of RR in software engineering research and present the methodology we have used to adopt RR principles. We report a small working example of how to create reproducible research. We summarise advantages of and problems with adopting RR methods. We conclude that RR supports good scientific practice and would help to address some of the problems found in empirical software engineering research.

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Section: Problems With Empirical Software Engineering Practicementioning

confidence: 99%

Would wider adoption of reproducible research be beneficial for empirical software engineering research?

Madeyski

Kitchenham

2017

IFS

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“…Miyazaki et al [36] employed robust regression (RoR) to predict effort of new project, which is an alternative to OLS regression with the advantage of being less vulnerable to the existence of outliers in the data. Recently, Whigham et al [9] addressed an automatically transformed linear model (ATLM) and recommended it as a baseline model for comparison against SEE methods.…”

Section: Effort Estimation Methodsmentioning

confidence: 99%

“…In recent years, several estimation methods have been presented [1][2][3][4][5][6][7][8][9][10]. Some well-known machine learning methods have also been employed for estimation, such as classification and regression tree (CART) [11] and neural networks (NN) [2,12].…”

Section: Introductionmentioning

confidence: 99%

Missing data imputation based on low-rank recovery and semi-supervised regression for software effort estimation

Jing

et al. 2016

Proceedings of the 38th International Conference on Software Engineering

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Software effort estimation (SEE) is a crucial step in software development. Effort data missing usually occurs in real-world data collection. Focusing on the missing data problem, existing SEE methods employ the deletion, ignoring, or imputation strategy to address the problem, where the imputation strategy was found to be more helpful for improving the estimation performance. Current imputation methods in SEE use classical imputation techniques for missing data imputation, yet these imputation techniques have their respective disadvantages and might not be appropriate for effort data. In this paper, we aim to provide an effective solution for the effort data missing problem. Incompletion includes the drive factor missing case and effort label missing case. We introduce the low-rank recovery technique for addressing the drive factor missing case. And we employ the semi-supervised regression technique to perform imputation in the case of effort label missing. We then propose a novel effort data imputation approach, named low-rank recovery and semisupervised regression imputation (LRSRI). Experiments on 7 widely used software effort datasets indicate that: (1) the proposed approach can obtain better effort data imputation effects than other methods; (2) the imputed data using our approach can apply to multiple estimators well.

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“…The estimation model considered in this study is the Automatically Transformed Linear Model (ATLM) proposed by Whigham et al [3]. ATLM was developed using multiple linear regression with a minimal threshold to act as a baseline for software effort estimation.…”

Section: B Experimental Setup and Evaluation Measuresmentioning

confidence: 99%

“…We employed the Mean Absolute Error (MAE) which has been proven reliable by Foss et al [9] and considered in previous studies [3][10] [2]. MAE is a risk function that measures the average absolute deviation of the estimated effort values from the true effort values.…”

Section: B Experimental Setup and Evaluation Measuresmentioning

confidence: 99%

A Stratification and Sampling Model for Bellwether Moving Window

Mensah

Keung

Bosu³

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-An effective method for finding the relevant number (window size) and the elapsed time (window age) of recently completed projects has proven elusive in software effort estimation. Although these two parameters significantly affect the prediction accuracy, there is no effective method to stratify and sample chronological projects to improve prediction performance of software effort estimation models. Exemplary projects (Bellwether) representing the training set have been empirically validated to improve the prediction accuracy in the domain of software defect prediction. However, the concept of Bellwether and its effect have not been empirically proven in software effort estimation as a method of selecting exemplary/relevant projects with defined window size and age. In view of this, we introduce a novel method for selecting relevant and recently completed projects referred to as Bellwether moving window for improving the software effort prediction accuracy. We first sort and cluster a pool of N projects and apply statistical stratification based on Markov chain modeling to select the Bellwether moving window. We evaluate the proposed approach using the baseline Automatically Transformed Linear Model on the ISBSG dataset. Results show that (1) Bellwether effect exist in software effort estimation dataset, (2) the Bellwether moving window with a window size of 82 to 84 projects and window age of 1.5 to 2 years resulted in an improved prediction accuracy than the traditional approach.

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A Baseline Model for Software Effort Estimation

Cited by 86 publications

References 47 publications

Would wider adoption of reproducible research be beneficial for empirical software engineering research?

Would wider adoption of reproducible research be beneficial for empirical software engineering research?

Missing data imputation based on low-rank recovery and semi-supervised regression for software effort estimation

A Stratification and Sampling Model for Bellwether Moving Window

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