NDLI: SimLatte: A Framework to Support Testing for Worst-Case Interrupt Latencies in Embedded Software

Content Provider	IEEE Xplore Digital Library
Author	Tingting Yu Srisa-an, W. Cohen, M.B. Rothermel, G.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Univ. of Nebraska, Lincoln, NE, USA (Tingting Yu; Srisa-an, W.; Cohen, M.B.; Rothermel, G.)
Abstract	Embedded systems tend to be interrupt-driven, yet the presence of interrupts can affect system dependability because there can be delays in servicing interrupts. Such delays can occur when multiple interrupt service routines and interrupts of different priorities compete for resources on a given CPU. For this reason, researchers have sought approaches by which to estimate worst-case interrupt latencies (WCILs) for systems. Most existing approaches, however, are based on static analysis. In this paper, we present SIMLATTE, a testing-based approach for finding WCILs. SIMLATTE uses a genetic algorithm for test case generation that converges on a set of inputs and interrupt arrival points that are likely to expose WCILs. It also uses an opportunistic interrupt invocation approach to invoke interrupts at a variety of feasible locations. Our evaluation of SIMLATTE on several non-trivial embedded systems reveals that it is considerably more effective and efficient than random testing. We also determine that the combination of the genetic algorithm and opportunistic interrupt invocation allows SIMLATTE to perform better than it can when using either one in isolation.
Starting Page	313
Ending Page	322
File Size	901562
Page Count	10
File Format	PDF
ISBN	9781479922550
DOI	10.1109/ICST.2014.44
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-03-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Embedded Software Sociology Genetic Algorithm Interrupt Latencies Generators Delays Statistics Biological cells Testing Genetic algorithms
Content Type	Text
Resource Type	Article

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