Infrared: A Meta Bug Detector

The recent breakthroughs in deep learning methods have sparked a wave of interest in learning-based bug detectors. Compared to the traditional static analysis tools, these bug detectors are directly learned from data, thus, easier to create. On the other hand, they are difficult to train, requiring a large amount of data which is not readily available. In this paper, we propose a new approach, called meta bug detection, which offers three crucial advantages over existing learning-based bug detectors: bug-type generic (i.e., capable of catching the types of bugs that are totally unobserved during training), self-explainable (i.e., capable of explaining its own prediction without any external interpretability methods) and sample efficient (i.e., requiring substantially less training data than standard bug detectors). Our extensive evaluation shows our meta bug detector (MBD) is effective in catching a variety of bugs including null pointer dereference, array index out-of-bound, file handle leak, and even data races in concurrent programs; in the process MBD also significantly outperforms several noteworthy baselines including Facebook Infer, a prominent static analysis tool, and FICS, the latest anomaly detection method

Calculation Method of Probability Integration Method Parameters Based on MIV-GP-BP Model

In order to guarantee the precision of the parameters of the probability integral method (PIM), starting from optimizing input and improving algorithm an algorithm integrating the genetic algorithm (GA) and particle swarm optimization (PSO) was put forward to optimize the prediction model of BP neural network and the mean impact value algorithm (MIV) was applied to optimize the input of BP neural network. The mean impact value algorithm (MIV) was applied to optimize the input of BP neural network. The measured data of 50 working faces were chosen as the training and testing sets to build the MIV-GP-BP model. The results showed that among the five parameters, the RMSE was between 0.0058 and 1.1575, the MaxRE of q, tanβ, b and θ was less than 5.42%, and the MeaRE was less than 2.81%. The RMSE of s/H did not exceed 0.0058, the MaxRE was less than 9.66% and the MeaRE was less than 4.31% (the parameters themselves were small). The optimized neural network model had higher prediction accuracy and stability

Artefactual literacy-related abilities for historians’ effective seeking and use of primary resources

This study explored historians’ archival search behaviour from Yakel and Torres’ User Expertise in Archives (UEA) model. The model contained three types of knowledge that influence archival researchers’ information seeking: domain knowledge, artefactual literacy, and archival intelligence. This paper reported on the artefactual literacy-related abilities and behaviour. A naturalistic inquiry approach was adopted to study twelve history master’s students’ thesis research behaviour. In-depth interviewing was used to collect narratives of archival search experiences. The inductive constant comparisons and open/axial coding were used in the analysis. Artefactual literacy related abilities and behaviour were categorized into external criticism-related and internal criticism-related. The former included three abilities: understanding the production context of primary sources, the ability to differentiate between intentional and unintentional sources, and the ability to cope with language limitations. The latter included two abilities: putting oneself in the shoe of the historical figure understudy, and comparing documents with different perspectives. UEA is a powerful analytic model for studying archive users’ information behaviour. Archivists should care about how domain knowledge and artefactual literacy influenced archival search decisions and actions.Peer Reviewe