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Dynamic analysis helps to extract important information about software systems useful in testing, debugging and maintenance activities. Popular dynamic analysis techniques synthesize either information on the values of the variables or information on relations between invocation of methods. Thus, these approaches do not capture the important relations that exist between data values and invocation sequences.In this paper, we introduce a technique, called GK-tail, for generating models that represent the interplay between program variables and method invocations. GK-tail extends the k-tail algorithm for extracting finite state automata from execution traces, to the case of finite state automata with parameters.The paper presents the technique and the results of some preliminary experiments that indicate the potentialities of the proposed approach.

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Enterprise systems must guarantee high availability and reliability to provide 24/7 services without interruptions and failures. Mechanisms for handling exceptional cases and implementing fault tolerance techniques can reduce failure occurrences, and increase dependability. Most of such mechanisms address major problems that lead to unexpected service termination or crashes, but do not deal with many subtle domain dependent failures that do not necessarily cause service termination or crashes, but result in incorrect results.In this paper, we propose a technique for developing selfprotecting systems. The technique proposed in this paper observes values at relevant program points. When the technique detects a software failure, it uses the collected information to identify the execution contexts that lead to the failure, and automatically enables mechanisms for preventing future occurrences of failures of the same type. Thus, failures do not occur again after the first detection of a failure of the same type.

This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract The SERENITY monitoring framework offers mechanisms for diagnosing the causes of violations of security and dependability (S&D) properties and detecting potential violations of such properties, called "threats". Diagnostic information and threat detection are often necessary for deciding what an appropriate reaction to a violation is and taking pre-emptive actions against predicted violations, respectively. In this chapter, we describe the mechanisms of the SERENITY monitoring framework which generate diagnostic information for violations of S&D properties and detecting threats. Permanent