Experience Report: Evaluating Fault Detection Effectiveness and Resource Efficiency of the Architecture Quality Assurance Framework and Tool

The Architecture Quality Assurance Framework (AQAF) is a theory developed to provide a holistic and formal verification process for architectural engineering of critical embedded systems. AQAF encompasses integrated architectural model checking, model-based testing, and selective regression verifica...

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Vydáno v:Proceedings - International Symposium on Software Reliability Engineering s. 271 - 281
Hlavní autoři: Johnsen, Andreas, Lundqvist, Kristina, Hanninen, Kaj, Pettersson, Paul, Torelm, Martin
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2017
Témata:
Automata
case study
Computer architecture
empirical evaluation
Fault detection
fault injection
Model checking
Observers
Quality assurance
Relays
ISSN:2332-6549
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Popis
Shrnutí:The Architecture Quality Assurance Framework (AQAF) is a theory developed to provide a holistic and formal verification process for architectural engineering of critical embedded systems. AQAF encompasses integrated architectural model checking, model-based testing, and selective regression verification techniques to achieve this goal. The Architecture Quality Assurance Tool (AQAT) implements the theory of AQAF and enables automated application of the framework. In this paper, we present an evaluation of AQAT and the underlying AQAF theory by means of an industrial case study, where resource efficiency and fault detection effectiveness are the targeted properties of evaluation. The method of fault injection is utilized to guarantee coverage of fault types and to generate a data sample size adequate for statistical analysis. We discovered important areas of improvement in this study, which required further development of the framework before satisfactory results could be achieved. The final results present a 100% fault detection rate at the design level, a 98.5% fault detection rate at the implementation level, and an average increased efficiency of 6.4% with the aid of the selective regression verification technique.
ISSN:2332-6549
DOI:10.1109/ISSRE.2017.31