An Empirical Investigation to Overcome Class-Imbalance in Inspection Reviews

@article{Singh2017AnEI,
  title={An Empirical Investigation to Overcome Class-Imbalance in Inspection Reviews},
  author={Maninder Singh and Gursimran Singh Walia and Anurag Goswami},
  journal={2017 International Conference on Machine Learning and Data Science (MLDS)},
  year={2017},
  pages={15-22}
}
Background: software inspection results in reviews that report the presence of faults. Requirements author must manually read through the reviews and differentiate between true-faults and false-positives. Problem: post-inspection decisions (fault or nonfault) are difficult and time consuming. It is difficult to employ machine learning (ML) techniques directly to raw (unstructured) data because of class imbalance problem and possible fault-slippage through misclassification of fault. Aim: The… Expand
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References

SHOWING 1-10 OF 28 REFERENCES
An Empirical Study on Improving Severity Prediction of Defect Reports Using Feature Selection
TLDR
Whether feature selection can benefit the severity prediction task with three commonly used feature selection schemes, Information Gain, Chi-Square, and Correlation Coefficient, based on the Multinomial Naive Bayes classification approach is discussed. Expand
Combining text mining and data mining for bug report classification
TLDR
A multi‐stage approach by combining both text mining and data mining techniques to automate the prediction process of bug reports, and empirically studied the impact relation between the underlying classifiers and various other properties of the combined model. Expand
Comprehensible software fault and effort prediction: A data mining approach
TLDR
Surprisingly, the trees extracted from the black-box models by ALPA are not only comprehensible and explain how theblack-box model makes (most of) its predictions, but are also more accurate than the trees obtained by working directly on the data. Expand
Solving the class imbalance problems using RUSMultiBoost ensemble
TLDR
This work proposes RUSMultiBoost, a hybrid method that is constituent of MultiBoost ensemble and random undersampling (RUS) to solve the class imbalance problem and shows that the hybrid ensemble method performs significantly better than other methods on benchmark data sets using G-mean, Sensitivity and F1-measure. Expand
Characteristics of Useful Code Reviews: An Empirical Study at Microsoft
TLDR
The proportion of useful comments made by a reviewer increases dramatically in the first year that he or she is at Microsoft but tends to plateau afterwards, and it is found that the more files that are in a change, the lower the proportion of comments in the code review that will be of value to the author of the change. Expand
Evaluating the Use of Requirement Error Abstraction and Classification Method for Preventing Errors during Artifact Creation: A Feasibility Study
TLDR
The hypothesis was that participants who find more errors during the inspection of a requirements document would make fewer errors when creating their own requirements document, and the overall result supports this hypothesis. Expand
Classification of defect types in requirements specifications: Literature review, proposal and assessment
TLDR
Recommendations are given to industry and other researchers on the design of classification schemes and treatment of classification results, following rules to build defects taxonomies. Expand
Models for evaluating review effectiveness
Delivering a high quality reliable product is the main focus in any software development. The basic quality measure is the defects in the product. Defects found in the later phases of the productExpand
Analysis of user comments: An approach for software requirements evolution
TLDR
This paper explores the rich set of user feedback available for third party mobile applications as a way to extract new/changed requirements for next versions by adapting information retrieval techniques including topic modeling and evaluating them on different publicly available data sets. Expand
A comparative study on sampling techniques for handling class imbalance in streaming data
  • Hien M. Nguyen, E. Cooper, K. Kamei
  • Computer Science
  • The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems
  • 2012
TLDR
This study suggests that a multiple random under-sampling (MRUS) technique should be a good choice for applications with imbalanced and streaming data, because MRUS is the most effective while still keeping a high speed. Expand
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