Discovering models of software processes from event-based data
ACM Transactions on Software Engineering and Methodology (TOSEM)
Experimentation in software engineering: an introduction
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Semantic anomaly detection in online data sources
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Dynamic Analysis of Software Systems using Execution Pattern Mining
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On the Synthesis of Finite-State Machines from Samples of Their Behavior
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A Systematic Survey of Program Comprehension through Dynamic Analysis
IEEE Transactions on Software Engineering
Inferring Finite-State Models with Temporal Constraints
ASE '08 Proceedings of the 2008 23rd IEEE/ACM International Conference on Automated Software Engineering
Computing the Structural Difference between State-Based Models
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Alattin: Mining Alternative Patterns for Detecting Neglected Conditions
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Inferring Resource Specifications from Natural Language API Documentation
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Mining Hierarchical Scenario-Based Specifications
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SEIM: static extraction of interaction models
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Using dynamic execution traces and program invariants to enhance behavioral model inference
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Generating test cases for specification mining
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A framework for the evaluation of specification miners based on finite state machines
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Synoptic: summarizing system logs with refinement
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Dynamic Analysis for Diagnosing Integration Faults
IEEE Transactions on Software Engineering
Reverse Engineering of Protocols from Network Traces
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LearnLib: a library for automata learning and experimentation
FASE'06 Proceedings of the 9th international conference on Fundamental Approaches to Software Engineering
Automatic synthesis of modular connectors via composition of protocol mediation patterns
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Producing software by integration: challenges and research directions (keynote)
Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering
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A number of techniques that infer finite state automata from execution traces have been used to support test and analysis activities. Some of these techniques can produce automata that integrate information about the data-flow, that is, they also represent how data values affect the operations executed by programs. The integration of information about operation sequences and data values into a unique model is indeed conceptually useful to accurately represent the behavior of a program. However, it is still unclear whether handling heterogeneous types of information, such as operation sequences and data values, necessarily produces higher quality models or not. In this paper, we present an empirical comparative study between techniques that infer simple automata and techniques that infer automata extended with information about data-flow. We investigate the effectiveness of these techniques when applied to traces with different levels of sparseness, produced by different software systems. To the best of our knowledge this is the first work that quantifies both the effect of adding data-flow information within automata and the effectiveness of the techniques when varying sparseness of traces.