Reverse Engineering Utility Functions Using Genetic Programming to Detect Anomalous Behavior in Software

Wong, Sunny; Aaron, M. R.; Segall, Jeffrey E.; Lynch, Kevin; Mancoridis, Spiros

doi:10.1109/wcre.2010.23

Cited by 9 publications

(8 citation statements)

References 25 publications

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“…The work by Wong et al [24] describes a evolutionary technique that uses genetic programming to automatically evolve an accurate utility function for a specific system, set of resource usage metrics, and precision recall reference. As in the case in question (as here), metrics are computed using sensor values that monitor a variety of system resources (e.g., memory usage, processor usage, thread count).…”

Section: Resultsmentioning

confidence: 99%

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Monitoring applications: An immune inspired algorithm for software-fault detection

Ligeiro

2014

Applied Soft Computing

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Section: Resultsmentioning

confidence: 99%

“…To the best of the author's knowledge, the only work presenting an evolutionary technique that also invokes a set of resource usage metrics for software faults detection is that by Wong et al [24]. Due to this similarity, their approach will be discussed in more detail in the Section 4.…”

Section: Preliminary Knowledgementioning

confidence: 99%

Monitoring applications: An immune inspired algorithm for software-fault detection

Ligeiro

2014

Applied Soft Computing

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“…For example, Wong et al [27] used a genetic algorithm for detecting software faults and anomalous behavior related to the resource usage of a system (e.g., memory usage, processor usage, thread count). Their approach is based on utility functions, which correspond to predicates that identify suspicious behavior based on resource usage metrics.…”

Section: Related Workmentioning

confidence: 99%

Specification and Detection of SOA Antipatterns

Palma

2014

2014 IEEE International Conference on Software Maintenance and Evolution

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Abstract. Like any other complex software system, Service Based Systems (SBSs) must evolve to fit new user requirements and execution contexts. The changes resulting from the evolution of SBSs may degrade their design and quality of service (QoS) and may often cause the appearance of common poor solutions, called Antipatterns. Antipatterns resulting from these changes also hinder the future maintenance and evolution of SBSs. The automatic detection of antipatterns is thus important to assess the design and QoS of SBSs and ease their maintenance and evolution. However, methods and techniques for the detection of antipatterns in SBSs are still in their infancy despite their importance. In this paper, we introduce a novel and innovative approach supported by a framework for specifying and detecting antipatterns in SBSs. Using our approach, we specify 10 well-known and common antipatterns, including Multi Service and Tiny Service, and we automatically generate their detection algorithms. We apply and validate the detection algorithms in terms of precision and recall on Home-Automation, an SBS developed independently. This validation demonstrates that our approach enables the specification and detection of SOA antipatterns with the precision of more than 90% and the recall of 100%.

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“…For example, Wong et al [29] used a genetic algorithm for detecting software faults and anomalous behavior related to the resource usage of a system (e.g., memory usage, processor usage, thread count). Their approach is based on utility functions, which correspond to predicates that identify suspicious behavior based on resource usage metrics.…”

Section: Detection Of Oo Antipatternsmentioning

confidence: 99%

Soa Antipatterns: An Approach for Their Specification and Detection

Palma

Nayrolles

Moha

et al. 2013

Int. J. Coop. Info. Syst.

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Like any other large and complex software systems, Service Based Systems (SBSs) must evolve to fit new user requirements and execution contexts. The changes resulting from the evolution of SBSs may degrade their design and quality of service (QoS) and may often cause the appearance of common poor solutions in their architecture, called antipatterns, in opposition to design patterns, which are good solutions to recurring problems. Antipatterns resulting from these changes may hinder the future maintenance and evolution of SBSs. The detection of antipatterns is thus crucial to assess the design and QoS of SBSs and facilitate their maintenance and evolution. However, methods and techniques for the detection of antipatterns in SBSs are still in their infancy despite their importance. In this paper, we introduce a novel and innovative approach supported by a framework for specifying and detecting antipatterns in SBSs. Using our approach, we specify ten well-known and common antipatterns, including Multi Service and Tiny Service, and automatically generate their detection algorithms. We apply and validate the detection algorithms in terms of precision and recall on two systems developed independently, (1) Home-Automation, an SBS with 13 services, and (2) FraSCAti, an open-source implementation of the Service Component Architecture (SCA) standard with more than 100 services. This validation demonstrates that our approach enables the specification and detection of SOA antipatterns with an average precision of 90% and recall of 97.5%.

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Reverse Engineering Utility Functions Using Genetic Programming to Detect Anomalous Behavior in Software

Cited by 9 publications

References 25 publications

Monitoring applications: An immune inspired algorithm for software-fault detection

Monitoring applications: An immune inspired algorithm for software-fault detection

Specification and Detection of SOA Antipatterns

Soa Antipatterns: An Approach for Their Specification and Detection

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