Are Smell-Based Metrics Actually Useful in Effort-Aware Structural Change-Proneness Prediction? An Empirical Study

Bad code smells (also named as code smells) are symptoms of poor design choices in implementation. Existing studies empirically confirmed that the presence of code smells increases the likelihood of subsequent changes (i.e., change-proness). However, to the best of our knowledge, no prior studies have leveraged smell-based metrics to predict particular change type (i.e., structural changes). Moreover, when evaluating the effectiveness of smell-based metrics in structural change-proneness prediction, none of existing studies take into account of the effort inspecting those change-prone source code. In this paper, we consider five smell-based metrics for effort-aware structural change-proneness prediction and compare these metrics with a baseline of well-known CK metrics in predicting particular categories of change types. Specifically, we first employ univariate logistic regression to analyze the correlation between each smellbased metric and structural change-proneness. Then, we build multivariate prediction models to examine the effectiveness of smell-based metrics in effort-aware structural change-proneness prediction when used alone and used together with the baseline metrics, respectively. Our experiments are conducted on six Java open-source projects with up to 60 versions and results indicate that: (1) all smell-based metrics are significantly related to structural change-proneness, except metric ANS in hive and SCM in camel after removing confounding effect of file size; (2) in most cases, smell-based metrics outperform the baseline metrics in predicting structural change-proneness; and (3) when used together with the baseline metrics, the smell-based metrics are more effective to predict change-prone files with being aware of inspection effort

Quantitative Analysis of Urban Regional Traffic Status

In order to monitor the real-time operation condition of urban region traffic flow, and to quickly identify regional traffic status, this paper adopts CNM (Clauset-Newman-Moore) Community Division Method of Complex Network to analyze traffic status information deeply implied from the regional road network traffic flow data, which aims to objectively develop the reasonable classification of regional traffic state with no classification criteria of determining regional traffic state. Combined with the regional road network traffic data from a certain city, the example analysis shows that this proposed method can easily provide the reasonable division of regional traffic condition and verifies the feasibility of the regional traffic state classification method. Besides, the example analysis gives the rough regional traffic status determination standard, laying theoretical basis for accurately judging the regional traffic state

Quantitative spectroscopic analysis of heterogeneous mixtures: the correction of multiplicative effects caused by variations in physical properties of samples

Spectral measurements of complex heterogeneous types of mixture samples are often affected by significant multiplicative effects resulting from light scattering, due to physical variations (e.g. particle size and shape, sample packing and sample surface, etc.) inherent within the individual samples. Therefore, the separation of the spectral contributions due to variations in chemical compositions from those caused by physical variations is crucial to accurate quantitative spectroscopic analysis of heterogeneous samples. In this work, an improved strategy has been proposed to estimate the multiplicative parameters accounting for multiplicative effects in each measured spectrum, and hence mitigate the detrimental influence of multiplicative effects on the quantitative spectroscopic analysis of heterogeneous samples. The basic assumption of the proposed method is that light scattering due to physical variations has the same effects on the spectral contributions of each of the spectroscopically active chemical component in the same sample mixture. Based on this underlying assumption, the proposed method realizes the efficient estimation of the multiplicative parameters by solving a simple quadratic programming problem. The performance of the proposed method has been tested on two publicly available benchmark data sets (i.e. near-infrared total diffuse transmittance spectra of four-component suspension samples and near infrared spectral data of meat samples) and compared with some empirical approaches designed for the same purpose. It was found that the proposed method provided appreciable improvement in quantitative spectroscopic analysis of heterogeneous mixture samples. The study indicates that accurate quantitative spectroscopic analysis of heterogeneous mixture samples can be achieved through the combination of spectroscopic techniques with smart modeling methodology

Liquid-gas Phase Transition in Strange Hadronic Matter with Weak Y-Y Interaction

The liquid-gas phase transition in strange hadronic matter is reexamined by using the new parameters about the $\Lambda - \Lambda$ interaction deduced from recent observation of $^{6}_{\Lambda\Lambda}He$ double hypernucleus. The extended Furnstahl-Serot-Tang model with nucleons and hyperons is utilized. The binodal surface, the limit pressure, the entropy, the specific heat capacity and the Caloric curves are addressed. We find that the liquid-gas phase transition can occur more easily in strange hadronic matter with weak Y-Y interaction than that of the strong Y-Y interaction.Comment: 10 pages, 7 figure

Implementation of a Social Network Information Dissemination Model Incorporating Negative Relationships

For the study of information dissemination in online social networks, most existing information dissemination models include only positive relationships, ignoring the existence and importance of negative relationships, and do not consider the influence of inter-individual relationship polarity on dissemination. To solve these problems, we propose a social network information dissemination model incorporating negative relationships in this paper. Drawing on the state concept of the SIR (Susceptible Infected Recovered) model, the three types of SIR states are subdivided into five sub-states. Combining the advantages of the viewpoint evolution model, the influence of relational polarity on node attitudes is added to the modeling of the propagation process. The experiment proves that the method proposed in this paper can show more specifically the changing trend in the number of propagation nodes with different attitudes and portray the process of information propagation in online social networks

A Method for Learning a Petri Net Model Based on Region Theory

The deployment of robots in real life applications is growing. For better control and analysis of robots, modeling and learning are the hot topics in the field. This paper proposes a method for learning a Petri net model from the limited attempts of robots. The method can supplement the information getting from robot system and then derive an accurate Petri net based on region theory accordingly. We take the building block world as an example to illustrate the presented method and prove the rationality of the method by two theorems. Moreover, the method described in this paper has been implemented by a program and tested on a set of examples. The results of experiments show that our algorithm is feasible and effective