Fault Detection in C Programs using Monitoring of Range Values: Preliminary Results

TL;DR

This paper introduces a fault detection method for C programs that monitors variable range values during runtime, using a source-to-source compiler and aspect-oriented strategies, with preliminary results showing promising accuracy.

Contribution

It develops a novel approach combining source-to-source compilation and AOP for runtime fault detection in C programs based on variable range monitoring.

Findings

Achieved 54% prediction accuracy on ABS

Achieved 83% prediction accuracy on GZIP

Potential but limited by array and control variable handling

Abstract

This technical report presents the work done as part of the AutoSeer project. Our work in this project was to develop a source-to-source compiler, MANET, for the C language that could be used for instrumentation of critical parts of applications under testing. The intention was to guide the compilation flow and define instrumentation strategies using the Aspect-Oriented Approach provided by LARA. This allows a separation of the original target application and the instrumentation secondary concerns. One of the goals of this work was the development of a source-to-source C compiler that modifies code according to an input strategy. These modifications could provide code transformations that target performance and instrumentation for debugging, but in this work they are used to inject code that collects information about the values that certain variables take during runtime. This compiler is supported by an AOP approach that enables the definition of instrumentation strategies. We decided to extend an existing source-to-source compiler, Cetus, and couple it with LARA, an AOP language that is partially abstracted from the target programming language. We propose and evaluate an approach to detect faults in C programs by monitoring the range values of variables. We consider various monitoring strategies and use two real-life applications, the GZIP file compressor and ABS, a program provided by an industrial partner. The different strategies were specified in LARA and automatically applied using MANET. The experimental results show that our approach has potential but is hindered by not accounting for values in arrays and control variables. We achieve prediction accuracies of around 54% for ABS and 83% for GZIP, when comparing our approach to a more traditional one, where the outputs are compared to an expected result.