Monitoring software requirements using instrumented code

215

114

Abstract. Information systems are facing conflicting requirements. On the one hand, systems need to be adaptive and self-managing to deal with rapidly changing circumstances. On the other hand, legislation such as the Sarbanes-Oxley Act, is putting increasing demands on monitoring activities and processes. As processes and systems become more flexible, both the need for, and the complexity of monitoring increases. Our earlier work on process mining has primarily focused on process discovery, i.e., automatically constructing models describing knowledge extracted from event logs. In this paper, we present another approach supported by our ProM framework and based on both a standard XML format and temporal logic. Given an event log and a property, our LTL Checker verifies whether the observed behavior matches the (un)expected/(un)desirable behavior.

Section: Related Workmentioning

confidence: 99%

“…The work Robinson [31,32] on requirements engineering is highly related. He suggests the use of LTL for the verification of properties.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Process Mining and Verification of Properties: An Approach Based on Temporal Logic

Aalst

Beer

Dongen

2005

215

114

“…Feather, Fickas, and Robinson [7,8,16] developed frameworks for run-time monitoring of software requirements that support the instrumentation, diagnosis, and reconfiguration of the system. To leverage these frameworks, a requirements engineer must first model the system's requirements through a goal-based modeling language, such as KAOS [5], and identify assumptions and constraints that could become violated.…”

Section: Requirements Monitoringmentioning

confidence: 99%

“…This monitoring information enables a DAS to detect both requirements violations, as well as conditions conducive to their occurrence [7,8,16]. Utility functions have been successfully applied for self-assessment purposes in DASs [3,9,15,17].…”

Section: Introductionmentioning

confidence: 99%

Automatic Derivation of Utility Functions for Monitoring Software Requirements

Ramirez¹,

Cheng²

2011

Abstract. Utility functions can be used to monitor requirements of a dynamically adaptive system (DAS). More specifically, a utility function maps monitoring information to a scalar value proportional to how well a requirement is satisfied. Utility functions may be manually elicited by requirements engineers, or indirectly inferred through statistical regression techniques. This paper presents a goal-based requirements modeldriven approach for automatically deriving state-, metric-, and fuzzy logic-based utility functions for RELAXed goal models. State-and fuzzy logic-based utility functions are responsible for detecting requirements violations, and metric-based utility functions are used to detect conditions conducive to a requirements violation. We demonstrate the proposed approach by applying it to the goal model of an intelligent vehicle system (IVS) and use the derived utility functions to monitor the IVS under different environmental conditions at run time.

Multi-level Model-Based Self-diagnosis of Distributed Object-Oriented Systems

Haydarlou

Overeinder

Oey

et al. 2006

Self-healing relies on correct diagnosis of system malfunctioning. This paper presents a use-case based approach to self-diagnosis. Both a static and a dynamic model of a managed-system are distinguished with explicit functional, implementational, and operational knowledge of specific use-cases. This knowledge is used to define sensors to detect and localise anomalies at the same three levels, providing the input needed to perform informed diagnosis. The models presented can be used to automatically instrument existing distributed legacy systems. This research is supported by the NLnet Foundation, http://www.nlnet.nl, and Fortis Bank Netherlands, http://www.fortisbank.nl. 1 The terms system and application will be used interchangeably.