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

Enhancing Event Sequence Modeling with Contrastive Relational Inference

Neural temporal point processes(TPPs) have shown promise for modeling continuous-time event sequences. However, capturing the interactions between events is challenging yet critical for performing inference tasks like forecasting on event sequence data. Existing TPP models have focused on parameterizing the conditional distribution of future events but struggle to model event interactions. In this paper, we propose a novel approach that leverages Neural Relational Inference (NRI) to learn a relation graph that infers interactions while simultaneously learning the dynamics patterns from observational data. Our approach, the Contrastive Relational Inference-based Hawkes Process (CRIHP), reasons about event interactions under a variational inference framework. It utilizes intensity-based learning to search for prototype paths to contrast relationship constraints. Extensive experiments on three real-world datasets demonstrate the effectiveness of our model in capturing event interactions for event sequence modeling tasks.Comment: 6 pages, 2 figure

An “In-Depth” Description of the Small Non-coding RNA Population of Schistosoma japonicum Schistosomulum

Parasitic flatworms of the genus Schistosoma are the causative agents of schistosomiasis, which afflicts more than 200 million people yearly in tropical regions of South America, Asia and Africa. A promising approach to the control of this and many other diseases involves the application of our understanding of small non-coding RNA function to the design of safe and effective means of treatment. In a previous study, we identified five conserved miRNAs from the adult stage of Schistosoma japonicum. Here, we applied Illumina Solexa high-throughput sequencing methods (deep sequencing) to investigate the small RNAs expressed in S. japonicum schistosomulum (3 weeks post-infection). This has allowed us to examine over four million sequence reads including both frequently and infrequently represented members of the RNA population. Thus we have identified 20 conserved miRNA families that have orthologs in well-studied model organisms and 16 miRNA that appear to be specific to Schistosoma. We have also observed minor amounts of heterogeneity in both 3′ and 5′ terminal positions of some miRNA as well as RNA fragments resulting from the processing of miRNA precursor. An investigation of the genomic arrangement of the 36 identified miRNA revealed that seven were tightly linked in two clusters. We also identified members of the small RNA population whose structure indicates that they are part of an endogenously derived RNA silencing pathway, as evidenced by their extensive complementarities with retrotransposon and retrovirus-related Pol polyprotein from transposon

Anti-Wear Property of Laser Textured 42CrMo Steel Surface

In this work, laser processing technology was utilized to fabricate micro-textures on the surface of 42CrMo steel to improve its wear resistance under high load conditions and provide an effective method to solve the wear of tooth plates in oil drilling wellhead machinery. Firstly, the friction process of the textured components was conducted by finite element analysis. Additionally, various forms of textures were compared and measured by this method to optimize the shape and parameters of the patterns. Secondly, three types of texture shapes, such as micro-dimples, micro-grooves, and reticular grooves, were created on the surface of 42CrMo steel. Lastly, the tribological characteristics of the micro-textures were analyzed in the dry friction experiments. Compared with the untextured surface, the wear resistance of the textured 42CrMo steel has been improved, and the anti-wear property of the micro-dimples was better than micro-grooves and reticular grooves. Along the direction of friction sliding, the wear of the front end is more worn than the rear end. Micro-dimples with a diameter of 0.8 mm, a spacing of 1.2 mm, and an area occupancy of 34.8% were fabricated at an output power of 200 W and a frequency of 5 Hz. The wear of the textured surface has been reduced by more than 80% in the process of ring-block dry friction with a load of 50 N, a rotation speed of 35 r/min, and a time of 15 min. The wear mechanism is mainly abrasive wear. The results showed that the hardness of the surface could be improved by laser hardening. In addition, micro-dimples on 42CrMo steel can store abrasive particles, mitigate the formation of furrows and reduce the abrasive wear of tooth plates