Events Calendar

PhD- Defense - Gholamreza Safi

Date and Location: Tuesday, January 19th, 11:00 am at SAL 322.

Title: Detecting Anomalies in Event-Based Systems Through Static Analysis

PhD Candidate: Gholamreza Safi

Committee: Nenad Medvidovic (chair), William GJ Halfond, Sandeep Gupta(outside member)


The event-based paradigm allows developers to design and build systems that are highly flexible and can be easily adapted. There are two main complications that can occur in the systems that are based on this paradigm. The first complication concerns inter-component interactions. Events that are used by components for this purpose are sent, received, and processed nondeterministically, due to the systems' reliance on implicit invocation and implicit concurrency. This nondeterminism can lead to event anomalies, which occur when an event-based system receives multiple events that lead to the write of a shared field or memory location. Event anomalies can lead to unreliable, error-prone, and hard-to-debug behavior in an event-based system. The second complication concerns intra-component interactions that usually occur through method calls. Each sequence of method calls introduces an execution path to the system. It is possible that there exist multiple execution paths that are not accessing the same memory locations or sharing data but are unnecessarily synchronized with each other despite the fact that they can be executed concurrently. I call these situation synchronization anomalies.

To detect event anomalies, this dissertation presents a new static analysis technique, DEvA, for automatically Detecting Event Anomalies. DEvA has been evaluated on a set of open-source event-based systems against a state-of-the-art technique for detecting data races in multi-threaded systems and a recent technique for solving a similar problem with event processing in Android applications. DEvA exhibited high precision with respect to manually constructed ground truths and was able to locate event anomalies that had not been detected by the existing solutions.

Also, this dissertation presents a new static analysis technique, DSA, for automatically Detecting Synchronization Anomalies. I have evaluated DSA both empirically and analytically. My empirical evaluation shows that synchronization anomalies are a common problem and can occur in any randomly chosen system. Also, DSA is efficient and scalable while exhibiting high precision, meaning that there were no false positives in its results after being applied to fourteen subject systems. The analytical evaluation of DSA provides guidelines for the situations where removing a synchronization anomaly can be more beneficial. By removing just one synchronization anomaly from two of our subject systems based on the provided guidelines, there was an enhancement of 10% in the performance of those systems.

Location: Henry Salvatori Computer Science Center (SAL) - 322

Audiences: Everyone Is Invited

Contact: Lizsl De Leon