Name
Abstract | ||
|---|---|---|
Fault injection (FI) is an experimental methodology used in a wide range of scenarios for validating the fault resilience of applications, especially safety-critical ones. A sufficiently thoroughgoing evaluation produces a significant amount of data regarding the behavior of software components or entire systems in the presence of faults. The core questions that practitioners using fault injection face are 1) how to extract and represent information, 2) how to effectively analyze that data and how to utilize the gained knowledge to improve the FI process. Previous works addressing these questions relied mainly on ad hoc approaches. The current paper presents a modern view of these problems, preparing and executing the knowledge extraction by exploratory (big) data analysis, methods, and tools. A real use-case based on FI campaigns composed of thousands of fault injections into a virtualized system indicates the huge potential of the approach. The outcome is the discovery of an opportunity for a drastic speed-up of the FI process unrevealed by the traditional methodology. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/QRS.2018.00033 | 2018 IEEE International Conference on Software Quality, Reliability and Security (QRS) |
Keywords | Field | DocType |
Exploratory Data Analysis,Fault Injection,Dependability | Psychological resilience,Data mining,Data visualization,Computer science,Software,Knowledge extraction,Component-based software engineering,Exploratory data analysis,Fault injection | Conference |
ISBN | Citations | PageRank |
978-1-5386-7758-2 | 0 | 0.34 |
References | Authors | |
26 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frederico Cerveira | 1 | 13 | 3.62 |
| Imre Kocsis | 2 | 7 | 5.23 |
| Raul Barbosa | 3 | 110 | 19.08 |
| Henrique Madeira | 4 | 5 | 2.47 |
| András Pataricza | 5 | 514 | 55.25 |