Towards Effective Bug Triage with Software Data Reduction Techniques

International audienceSoftware companies spend over 45 percent of cost in dealing with software bugs. An inevitable step of fixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To decrease the time cost in manual work, text classification techniques are applied to conduct automatic bug triage. In this paper, we address the problem of data reduction for bug triage, i.e., how to reduce the scale and improve the quality of bug data. We combine instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word dimension. To determine the order of applying instance selection and feature selection, we extract attributes from historical bug data sets and build a predictive model for a new bug data set. We empirically investigate the performance of data reduction on totally 600,000 bug reports of two large open source projects, namely Eclipse and Mozilla. The results show that our data reduction can effectively reduce the data scale and improve the accuracy of bug triage. Our work provides an approach to leveraging techniques on data processing to form reduced and high-quality bug data in software development and maintenance

ADR-Miner: An Ant-based data reduction algorithm for classification

Classi cation is a central problem in the elds of data mining and machine learning. Using a training set of labeled instances, the task is to build a model (classi er) that can be used to predict the class of new unlabeled instances. Data preparation is crucial to the data mining process, and its focus is to improve the tness of the training data for the learning algorithms to produce more e ective classi ers. Two widely applied data preparation methods are feature selection and instance selection, which fall under the umbrella of data reduction. For my research I propose ADR-Miner, a novel data reduction algorithm that utilizes ant colony optimization (ACO). ADR-Miner is designed to perform instance selection to improve the predictive e ectiveness of the constructed classi cation models. Two versions of ADR-Miner are developed: a base version that uses a single classi cation algorithm during both training and testing, and an extended version which uses separate classi cation algorithms for each phase. The base version of the ADR-Miner algorithm is evaluated against 20 data sets using three classi cation algorithms, and the results are compared to a benchmark data reduction algorithm. The non-parametric Wilcoxon signed-ranks test will is employed to gauge the statistical signi cance of the results obtained. The extended version of ADR-Miner is evaluated against 37 data sets using pairings from fi ve classi cation algorithms and these results are benchmarked against the performance of the classi cation algorithms but without reduction applied as pre-processing. Keywords: Ant Colony Optimization (ACO), Data Mining, Classi cation, Data Reduction

Discriminatory Expressions to Produce Interpretable Models in Short Documents

Social Networking Sites (SNS) are one of the most important ways of communication. In particular, microblogging sites are being used as analysis avenues due to their peculiarities (promptness, short texts...). There are countless researches that use SNS in novel manners, but machine learning has focused mainly in classification performance rather than interpretability and/or other goodness metrics. Thus, state-of-the-art models are black boxes that should not be used to solve problems that may have a social impact. When the problem requires transparency, it is necessary to build interpretable pipelines. Although the classifier may be interpretable, resulting models are too complex to be considered comprehensible, making it impossible for humans to understand the actual decisions. This paper presents a feature selection mechanism that is able to improve comprehensibility by using less but more meaningful features while achieving good performance in microblogging contexts where interpretability is mandatory. Moreover, we present a ranking method to evaluate features in terms of statistical relevance and bias. We conducted exhaustive tests with five different datasets in order to evaluate classification performance, generalisation capacity and complexity of the model. Results show that our proposal is better and the most stable one in terms of accuracy, generalisation and comprehensibility

A concept drift-tolerant case-base editing technique

© 2015 Elsevier B.V. All rights reserved. The evolving nature and accumulating volume of real-world data inevitably give rise to the so-called "concept drift" issue, causing many deployed Case-Based Reasoning (CBR) systems to require additional maintenance procedures. In Case-base Maintenance (CBM), case-base editing strategies to revise the case-base have proven to be effective instance selection approaches for handling concept drift. Motivated by current issues related to CBR techniques in handling concept drift, we present a two-stage case-base editing technique. In Stage 1, we propose a Noise-Enhanced Fast Context Switch (NEFCS) algorithm, which targets the removal of noise in a dynamic environment, and in Stage 2, we develop an innovative Stepwise Redundancy Removal (SRR) algorithm, which reduces the size of the case-base by eliminating redundancies while preserving the case-base coverage. Experimental evaluations on several public real-world datasets show that our case-base editing technique significantly improves accuracy compared to other case-base editing approaches on concept drift tasks, while preserving its effectiveness on static tasks

A comparison of statistical machine learning methods in heartbeat detection and classification

In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms