On the accuracy of spectrum-based fault localization

Spectrum-based fault localization shortens the test- diagnose-repair cycle by reducing the debugging effort. As a light-weight automated diagnosis technique it can easily be integrated with existing testing schemes. However, as no model of the system is taken into account, its diagnostic accuracy is inherently limited. Using the Siemens Set benchmark, we investigate this diagnostic accuracy as a function of several parameters (such as quality and quantity of the program spectra collected during the execution of the system), some of which directly relate to test design. Our results indicate that the superior performance of a particular similarity coefficient, used to analyze the program spectra, is largely independent of test design. Furthermore, near- optimal diagnostic accuracy (exonerating about 80% of the blocks of code on average) is already obtained for low-quality error observations and limited numbers of test cases. The influence of the number of test cases is of primary importance for continuous (embedded) processing applications, where only limited observation horizons can be maintained

Diagnosis of embedded software using program spectra

Automated diagnosis of errors detected during software testing can improve the efficiency of the debugging process, and can thus help to make software more reliable. In this paper we discuss the application of a specific automated debugging technique, namely software fault localization through the analysis of program spectra, in the area of embedded software in high-volume consumer electronics products. We discuss why the technique is particularly well suited for this application domain, and through experiments on an industrial test case we demonstrate that it can lead to highly accurate diagnoses of realistic errors

Automatic systems diagnosis without behavioral models

Recent feedback obtained while applying Model-based diagnosis (MBD) in industry suggests that the costs involved in behavioral modeling (both expertise and labor) can outweigh the benefits of MBD as a high-performance diagnosis approach. In this paper, we propose an automatic approach, called ANTARES, that completely avoids behavioral modeling. Decreasing modeling sacrifices diagnostic accuracy, as the size of the ambiguity group (i.e., components which cannot be discriminated because of the lack of information) increases, which in turn increases misdiagnosis penalty. ANTARES further breaks the ambiguity group size by considering the component's false negative rate (FNR), which is estimated using an analytical expression. Furthermore, we study the performance of ANTARES for a number of logic circuits taken from the 74XXX/ISCAS benchmark suite. Our results clearly indicate that sacrificing modeling information degrades the diagnosis quality. However, considering FNR information improves the quality, attaining the diagnostic performance of an MBD approach

SPC: A Model of Parallel Computation

. A new model of parallel computation is presented that provides appropriate cost models for automatic systems parallelization. We demonstrate its utility in automatic optimization for vectorization and provide a reference to more elaborate examples. 1 Introduction In typical compile-time engines, optimization decisions, such as computation or communication pipelining and data distribution, are either hard-coded in terms of heuristic transformation schemes, or are simply left to programmer annotation. At a more generic level, a vast body of work exists on modeling parallel computations with the purpose of choosing between different algorithms or optimizations based on comparing the associated time costs. On the one hand, detailed cost prediction methods exist, ranging in flavor from performance simulation to static analysis [7]. Either the estimates are too unreliable or the computational complexity prohibits their use in automatic optimization. On the other hand, abstract models of p..

Compiling performance models from parallel programs

A technique is described to automatically compile performance models in the course of program translation. The performance models are fully symbolic in order to preserve as much diagnostic information as possible. Although compiled statically, the models account for the effects of resource contention, due to the introduction of a novel algorithm within the symbolic compilation scheme. It is shown that the compilation approach fundamentally outperforms traditional static estimation procedures in terms of precision at a negligible increase in cost. This claim is illustrated by a case study of an LU factorization algorithm on a multiprocessor. 1 Introduction Low-cost, compile-time performance prediction provides essential, early feedback to enable program and machine parameter optimization by both the user and the compiler. In this paper we present a technique to automatically compile a symbolic performance model which accurately predicts the execution time of a parallel program given a..