Software bugs often arise because of differences between what developers think their system does and what the system actually does. These differences frustrate debugging and comprehension efforts. We describe Perfume, an automated approach for inferring behavioral, resource-aware models of software systems from logs of their executions. These finite state machine models ease understanding of system behavior and resource use.Perfume improves on the state of the art in model inference by differentiating behaviorally similar executions that differ in resource consumption. For example, Perfume separates otherwise identical requests that hit a cache from those that miss it, which can aid understanding how the cache affects system behavior and removing cache-related bugs. A small user study demonstrates that using Perfume is more effective than using logs and another model inference tool for system comprehension. A case study on the TCP protocol demonstrates that Perfume models can help understand non-trivial protocol behavior. Perfume models capture key system properties and improve system comprehension, while being reasonably robust to noise likely to occur in real-world executions.

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Software bugs often arise from differences between what developers envision their system does and what that system actually does. When faced with such conceptual inconsistencies, debugging can be very difficult. Inferring and presenting developers with accurate behavioral models of the system implementation can help developers reconcile their view of the system with reality and improve system quality.We present Perfume, a model-inference algorithm that improves on the state of the art by using performance information to differentiate otherwise similar-appearing executions and to remove false positives from the inferred models. Perfume uses a system's runtime execution logs to infer a concise, precise, and predictive finite state machine model that describes both observed executions and executions that have not been observed but that the system can likely generate. Perfume guides the model inference process by mining temporal performance-constrained properties from the logs, ensuring precision of the model's predictions. We describe the model inference process and demonstrate how it improves precision over the state of the art.

Many APIs enable cross-platform system development by abstracting over the details of a platform, allowing application developers to write one implementation that will run on a wide variety of platforms. Unfortunately, subtle differences in the behavior of the underlying platforms make cross-platform behavior difficult to achieve. As a result, applications using these APIs can be plagued by bugs difficult to observe before deployment. These portability bugs can be particularly difficult to diagnose and fix because they arise from the API implementation, the operating system, or hardware, rather than application code.This paper describes CheckAPI, a technique for detecting violations of cross-platform portability. CheckAPI compares an application's interactions with the API implementation to its interactions with a partial specification-based API implementation, and does so efficiently enough to be used in real production systems and at runtime. CheckAPI finds latent errors that escape pre-release testing. This paper discusses the subtleties of different kinds of API calls and strategies for effectively producing the partial implementations. Validating CheckAPI on JavaScript, the Seattle project's Repy VM, and POSIX detects dozens of violations that are confirmed bugs in widely-used software.

Understanding how software utilizes resources is an important software engineering task. Existing software comprehension approaches rarely consider how resource utilization affects system behavior. We present Perfume, a general-purpose tool to help developers understand how resource utilization impacts their systems' control flow. Perfume is broadly applicable, as it is configurable to parse a wide variety of execution log formats and applies to all resource types that can be represented numerically. Perfume mines temporal properties that hold over the logged executions and represents system behavior in a resource finite state automaton that satisfies the mined properties. Perfume's interactive interface allows the developers to understand system behavior and to formulate and test hypotheses about system executions. A controlled experiment with 40 students shows that Perfume effectively supports understanding and debugging tasks. Students using Perfume answered 8.3% more questions correctly than those using execution logs alone and did so 15.5% more quickly. Perfume is open source and deployed at http://perfume.cs.umass.edu/. Perfume demo video: http://perfume.cs.umass.edu/demo CCS Concepts •Software and its engineering → Software system models; Abstraction, modeling and modularity; Dynamic analysis; Software maintenance tools; Model checking;