A Lightweight Visualization of Interprocedural Data-Flow Paths for Source Code Reading

Program Comprehension (ICPC), 2012 IEEE 20th International Conference onDate of Conference:11-13 June 2012Conference Location :Passa

An empirical investigation into branch coverage for C programs using CUTE and AUSTIN

Automated test data generation has remained a topic of considerable interest for several decades because it lies at the heart of attempts to automate the process of Software Testing. This paper reports the results of an empirical study using the dynamic symbolic-execution tool. CUTE, and a search based tool, AUSTIN on five non-trivial open source applications. The aim is to provide practitioners with an assessment of what can be achieved by existing techniques with little or no specialist knowledge and to provide researchers with baseline data against which to measure subsequent work. To achieve this, each tool is applied 'as is', with neither additional tuning nor supporting harnesses and with no adjustments applied to the subject programs under test. The mere fact that these tools can be applied 'out of the box' in this manner reflects the growing maturity of Automated test data generation. However, as might be expected, the study reveals opportunities for improvement and suggests ways to hybridize these two approaches that have hitherto been developed entirely independently. (C) 2010 Elsevier Inc. All rights reserved

Efficient and Effective Handling of Exceptions in Java Points-To Analysis

A joint points-to and exception analysis has been shown to yield benefits in both precision and performance. Treating exceptions as regular objects, however, incurs significant and rather unexpected overhead. We show that in a typical joint analysis most of the objects computed to flow in and out of a method are due to exceptional control-flow and not normal call-return control-flow. For instance, a context-insensitive analysis of the Antlr benchmark from the DaCapo suite computes 4-5 times more objects going in or out of a method due to exceptional control-flow than due to normal control-flow. As a consequence, the analysis spends a large amount of its time considering exceptions. We show that the problem can be addressed both e ectively and elegantly by coarsening the representation of exception objects. An interesting find is that, instead of recording each distinct exception object, we can collapse all exceptions of the same type, and use one representative object per type, to yield nearly identical precision (loss of less than 0.1%) but with a boost in performance of at least 50% for most analyses and benchmarks and large space savings (usually 40% or more)

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