arXiv subjects

Yutao Ma

Publications and source records attributed to Yutao Ma.

48 records · Page 3Linked to original sources

arXiv2016

TDSelector: A Training Data Selection Method for Cross-Project Defect Prediction

In recent years, cross-project defect prediction (CPDP) attracted much attention and has been validated as a feasible way to address the problem of local data sparsity in newly created or inactive software projects. Unfortunately, the performance of CPDP is usually poor, and low quality training data selection has been regarded as a major obstacle to achieving better prediction results. To the best of our knowledge, most of existing approaches related to this topic are only based on instance similarity. Therefore, the objective of this work is to propose an improved training data selection method for CPDP that considers both similarity and the number of defects each training instance has (denoted by defects), which is referred to as TDSelector, and to demonstrate the effectiveness of the proposed method. Our experiments were conducted on 14 projects (including 15 data sets) collected from two public repositories. The results indicate that, in a specific CPDP scenario, the TDSelector-based bug predictor performs, on average, better than those based on the baseline methods, and the AUC (area under ROC curve) values are increased by up to 10.6 and 4.3%, respectively. Besides, an additional experiment shows that selecting those instances with more bugs directly as training data can further improve the performance of the bug predictor trained by our method.

cs.SE↗

arXiv2015

Log-Sobolev, isoperimetry and transport inequalities on graphs

In this paper, we study some functional inequalities (such as Poincaré inequalities, logarithmic Sobolev inequalities, generalized Cheeger isoperimetric inequalities, transportation-information inequalities and transportation-entropy inequalities) for reversible nearest-neighbor Markov processes on a connected finite graph by means of (random) path method. We provide estimates of the involved constants.

math.PR↗

arXiv2015

A Hybrid Approach to Web Service Recommendation Based on QoS-Aware Rating and Ranking

As the number of Web services with the same or similar functions increases steadily on the Internet, nowadays more and more service consumers pay great attention to the non-functional properties of Web services, also known as quality of service (QoS), when finding and selecting appropriate Web services. For most of the QoS-aware Web service recommendation systems, the list of recommended Web services is generally obtained based on a rating-oriented prediction approach, aiming at predicting the potential ratings that an active user may assign to the unrated services as accurately as possible. However, in some application scenarios, high accuracy of rating prediction may not necessarily lead to a satisfactory recommendation result. In this paper, we propose a ranking-oriented hybrid approach by combining the item-based collaborative filtering and latent factor models to address the problem of Web services ranking. In particular, the similarity between two Web services is measured in terms of the correlation coefficient between their rankings instead of between the traditional QoS ratings. Besides, we also improve the measure NDCG (Normalized Discounted Cumulative Gain) for evaluating the accuracy of the top K recommendations returned in ranked order. Comprehensive experiments on the QoS data set composed of real-world Web services are conducted to test our approach, and the experimental results demonstrate that our approach outperforms other competing approaches.

cs.IR↗

arXiv2014

An Empirical Study on Software Defect Prediction with a Simplified Metric Set

Software defect prediction plays a crucial role in estimating the most defect-prone components of software, and a large number of studies have pursued improving prediction accuracy within a project or across projects. However, the rules for making an appropriate decision between within- and cross-project defect prediction when available historical data are insufficient remain unclear. The objective of this work is to validate the feasibility of the predictor built with a simplified metric set for software defect prediction in different scenarios, and to investigate practical guidelines for the choice of training data, classifier and metric subset of a given project. First, based on six typical classifiers, we constructed three types of predictors using the size of software metric set in three scenarios. Then, we validated the acceptable performance of the predictor based on Top-k metrics in terms of statistical methods. Finally, we attempted to minimize the Top-k metric subset by removing redundant metrics, and we tested the stability of such a minimum metric subset with one-way ANOVA tests. The experimental results indicate that (1) the choice of training data should depend on the specific requirement of prediction accuracy; (2) the predictor built with a simplified metric set works well and is very useful in case limited resources are supplied; (3) simple classifiers (e.g., Naive Bayes) also tend to perform well when using a simplified metric set for defect prediction; and (4) in several cases, the minimum metric subset can be identified to facilitate the procedure of general defect prediction with acceptable loss of prediction precision in practice. The guideline for choosing a suitable simplified metric set in different scenarios is presented in Table 12.

cs.SE↗

arXiv2014

A note on spectral gap and weighted Poincaré inequalities for some one-dimensional diffusions

We present some classical and weighted Poincaré inequalities for some one-dimensional probability measures. This work is the one-dimensional counterpart of a recent study achieved by the authors for a class of spherically symmetric probability measures in dimension larger than 2. Our strategy is based on two main ingredients: on the one hand, the optimal constant in the desired weighted Poincaré inequality has to be rewritten as the spectral gap of a convenient Markovian diffusion operator, and on the other hand we use a recent result given by the two first authors, which allows to estimate precisely this spectral gap. In particular we are able to capture its exact value for some examples.

math.PR↗

arXiv2014

Towards Cross-Project Defect Prediction with Imbalanced Feature Sets

Cross-project defect prediction (CPDP) has been deemed as an emerging technology of software quality assurance, especially in new or inactive projects, and a few improved methods have been proposed to support better defect prediction. However, the regular CPDP always assumes that the features of training and test data are all identical. Hence, very little is known about whether the method for CPDP with imbalanced feature sets (CPDP-IFS) works well. Considering the diversity of defect data sets available on the Internet as well as the high cost of labeling data, to address the issue, in this paper we proposed a simple approach according to a distribution characteristic-based instance (object class) mapping, and demonstrated the validity of our method based on three public defect data sets (i.e., PROMISE, ReLink and AEEEM). Besides, the empirical results indicate that the hybrid model composed of CPDP and CPDP-IFS does improve the prediction performance of the regular CPDP to some extent.

cs.SE↗

arXiv2014

Simplification of Training Data for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) plays an important role in estimating the most likely defect-prone software components, especially for new or inactive projects. To the best of our knowledge, few prior studies provide explicit guidelines on how to select suitable training data of quality from a large number of public software repositories. In this paper, we have proposed a training data simplification method for practical CPDP in consideration of multiple levels of granularity and filtering strategies for data sets. In addition, we have also provided quantitative evidence on the selection of a suitable filter in terms of defect-proneness ratio. Based on an empirical study on 34 releases of 10 open-source projects, we have elaborately compared the prediction performance of different defect predictors built with five well-known classifiers using training data simplified at different levels of granularity and with two popular filters. The results indicate that when using the multi-granularity simplification method with an appropriate filter, the prediction models based on Naive Bayes can achieve fairly good performance and outperform the benchmark method.

cs.SE↗

arXiv2014

An Analysis of Research in Software Engineering: Assessment and Trends

Glass published the first report on the assessment of systems and software engineering scholars and institutions two decades ago. The ongoing, annual survey of publications in this field provides fund managers, young scholars, graduate students, etc. with useful information for different purposes. However, the studies have been questioned by some critics because of a few shortcomings of the evaluation method. It is actually very hard to reach a widely recognized consensus on such an assessment of scholars and institutions. This paper presents a module and automated method for assessment and trends analysis in software engineering compared with the prior studies. To achieve a more reasonable evaluation result, we take into consideration more high-quality publications, the rank of each publication analyzed, and the different roles of authors named on each paper in question. According to the 7638 papers published in 36 publications from 2008 to 2013, the statistics of research subjects roughly follow power laws, implying the interesting Matthew Effect. We then identify the Top 20 scholars, institutions and countries or regions in terms of a new evaluation rule based on the frequently-used one. The top-ranked scholar is Mark Harman of the University College London, UK, the top-ranked institution is the University of California, USA, and the top-ranked country is the USA. Besides, we also show two levels of trend changes based on the EI classification system and user-defined uncontrolled keywords, as well as noteworthy scholars and institutions in a specific research area. We believe that our results would provide a valuable insight for young scholars and graduate students to seek possible potential collaborators and grasp the popular research topics in software engineering.

cs.SE↗

arXiv2014

Spectral gap for spherically symmetric log-concave probability measures, and beyond

Let $μ$ be a probability measure on $\rr^n$ ($n \geq 2$) with Lebesgue density proportional to $e^{-V (\Vert x\Vert )}$, where $V : \rr_+ \to \rr$ is a smooth convex potential. We show that the associated spectral gap in $L^2 (μ)$ lies between $(n-1) / \int_{\rr^n} \Vert x\Vert ^2 μ(dx)$ and $n / \int_{\rr^n} \Vert x\Vert ^2 μ(dx)$, improving a well-known two-sided estimate due to Bobkov. Our Markovian approach is remarkably simple and is sufficiently robust to be extended beyond the log-concave case, at the price of potentially modifying the underlying dynamics in the energy, leading to weighted Poincaré inequalities. All our results are illustrated by some classical and less classical examples.

math.PR↗

arXiv2013

Dynamics of Open-Source Software Developer's Commit Behavior: An Empirical Investigation of Subversion

Commit is an important operation of revision control for open-source software (OSS). Recent research has been pursued to explore the statistical laws of such an operation, but few of those papers conduct empirical investigations on commit interval (i.e., the waiting time between two consecutive commits). In this paper, we investigated software developer's collective and individual commit behavior in terms of the distribution of commit intervals, and found that 1) the data sets of project-level commit interval within both the lifecycle and each release of the projects analyzed roughly follow power-law distributions; and 2) lifecycle- and release-level collective commit interval on class files can also be best fitted with power laws. These findings reveal some general (collective) collaborative development patterns of OSS projects, e.g., most of the waiting times between two consecutive commits to a central repository are short, but only a few of them experience a long duration of waiting. Then, the implications of what we found for OSS research were outlined, which could provide an insight into understanding OSS development processes better based on software developers' historical commit behavior.

cs.SE↗

arXiv2011

Transportation inequalities: From Poisson to Gibbs measures

We establish an optimal transportation inequality for the Poisson measure on the configuration space. Furthermore, under the Dobrushin uniqueness condition, we obtain a sharp transportation inequality for the Gibbs measure on $\mathbb{N}^Λ$ or the continuum Gibbs measure on the configuration space.

math.ST↗

arXiv2008

Network Motifs in Object-Oriented Software Systems

Nowadays, software has become a complex piece of work that may be beyond our control. Understanding how software evolves over time plays an important role in controlling software development processes. Recently, a few researchers found the quantitative evidence of structural duplication in software systems or web applications, which is similar to the evolutionary trend found in biological systems. To investigate the principles or rules of software evolution, we introduce the relevant theories and methods of complex networks into structural evolution and change of software systems. According to the results of our experiment on network motifs, we find that the stability of a motif shows positive correlation with its abundance and a motif with high Z score tends to have stable structure. These findings imply that the evolution of software systems is based on functional cloning as well as structural duplication and tends to be structurally stable. So, the work presented in this paper will be useful for the analysis of structural changes of software systems in reverse engineering.

cs.SE↗