Event logs have been widely used over the last three decades to analyze the failure behavior of a variety of systems. Nevertheless, the implementation of the logging mechanism lacks a systematic approach and collected logs are often inaccurate at reporting software failures: This is a threat to the validity of log-based failure analysis. This paper analyzes the limitations of current logging mechanisms and proposes a rule-based approach to make logs effective to analyze software failures. The approach leverages artifacts produced at system design time and puts forth a set of rules to formalize the placement of the logging instructions within the source code. The validity of the approach, with respect to traditional logging mechanisms, is shown by means of around 12,500 software fault injection experiments into real-world systems

Practitioners widely recognize the importance of event logging for a variety of tasks, such as accounting, system measurements and troubleshooting. Nevertheless, in spite of the importance of the tasks based on the logs collected under real workload conditions, event logging lacks systematic design and implementation practices. The implementation of the logging mechanism strongly relies on the human expertise. This paper proposes a measurement study of event logging practices in a critical industrial domain. We assess a software development process at Selex ES, a leading Finmeccanica company in electronic and information solutions for critical systems. Our study combines source code analysis, inspection of around 2.3 millions log entries, and direct feedback from the development team to gain process-wide insights ranging from programming practices, logging objectives and issues impacting log analysis. The findings of our study were extremely valuable to prioritize event logging reengineering tasks at Selex ES

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Wireless Sensor Networks (WSNs) are widely recognized as a promising solution to build next-generation monitoring systems. Their industrial uptake is however still compromised by the low level of trust on their performance and dependability. Whereas analytical models represent a valid mean to assess nonfunctional properties via simulation, their wide use is still limited by the complexity and dynamicity of WSNs, which lead to unaffordable modeling costs. To reduce this gap between research achievements and industrial development, this paper presents a framework for the assessment of WSNs based on the automated generation of analytical models. The framework hides modeling details, and it allows designers to focus on simulation results to drive their design choices. Models are generated starting from a high-level specification of the system and by a preliminary characterization of its fault-free behavior, using behavioral simulators. The benefits of the framework are shown in the context of two case studies, based on the wireless monitoring of civil structures