Presented by Jonathan Bell at 33rd ACM/IEEE International Conference on Automated Software Engineering (ASE) in September 2018. https://doi.org/10.1145/3238147.3238186.

Software available at https://github.com/Programming-Systems-Lab/Parikshan. The software is not maintained.

Short time-to-localize and time-to-fix for production bugs is extremely important for any 24×7 service-oriented application (SOA). Debugging buggy behavior in deployed applications is hard, as it requires careful reproduction of a similar environment and workload. Prior approaches for automatically reproducing production failures do not scale to large SOA systems. Our key insight is that for many failures in SOA systems (e.g., many semantic and performance bugs), a failure can automatically be reproduced solely by relaying network packets to replicas of suspect services, an insight that we validated through a manual study of 16 real bugs across five different systems. This paper presents Parikshan, an application monitoring framework that leverages user-space virtualization and network proxy technologies to provide a sandbox “debug” environment. In this “debug” environment, developers are free to attach debuggers and analysis tools without impacting performance or correctness of the production environment. In comparison to existing monitoring solutions that can slow down production applications, Parikshan allows application monitoring at significantly lower overhead.

Presented by Anthony Saieva at ACM Workshop on Forming an Ecosystem Around Software Transformation (FEAST) on November 13, 2020. https://doi.org/10.1145/3411502.3418424

When applying patches, or dealing with legacy software, users are often reluctant to change the production executables for fear of unwanted side effects. This results in many active systems running vulnerable or buggy code even though the problems have already been identified and resolved by developers. Furthermore when dealing with old or proprietary software, users can’t view or compile source code so any attempts to change the application after distribution requires binary level manipulation. We present a new technique we call binary quilting that allows users to apply the designated minimum patch that preserves core semantics without fear of unwanted side effects introduced either by the build process or by additional code changes. Unlike hot patching, binary quilting is a one-time procedure that creates an entirely new reusable binary. Our case studies show the efficacy of this technique on real software in real patching scenarios.

Presented by Anthony Saieva at 20th IEEE International Working Conference on Source Code Analysis and Manipulation (SCAM) on September 27, 2020. https://doi.org/10.1109/SCAM51674.2020.00018

Software available at https://github.com/Programming-Systems-Lab/ATTUNE.

When a security vulnerability or other critical bug is not detected by the developers’ test suite, and is discovered post-deployment, developers must quickly devise a new test that reproduces the buggy behavior. Then the developers need to test whether their candidate patch indeed fixes the bug, without breaking other functionality, while racing to deploy before attackers pounce on exposed user installations. This can be challenging when factors in a specific user environment triggered the bug. If enabled, however, record-replay technology faithfully replays the execution in the developer environment as if the program were executing in that user environment under the same conditions as the bug manifested. This includes intermediate program states dependent on system calls, memory layout, etc. as well as any externally-visible behavior. Many modern record-replay tools integrate interactive debuggers, to help locate the root cause, but don’t help the developers test whether their patch indeed eliminates the bug under those same conditions. In particular, modern record-replay tools that reproduce intermediate program state cannot replay recordings made with one version of a program using a different version of the program where the differences affect program state. This work builds on record-replay and binary rewriting to automatically generate and run targeted tests for candidate patches significantly faster and more efficiently than traditional test suite generation techniques like symbolic execution. These tests reflect the arbitrary (ad hoc) user and system circumstances that uncovered the bug, enabling developers to check whether a patch indeed fixes that bug. The tests essentially replay recordings made with one version of a program using a different version of the program, even when the the differences impact program state, by manipulating both the binary executable and the recorded log to result in an execution consistent with what would have happened had the the patched version executed in the user environment under the same conditions where the bug manifested with the original version. Our approach also enables users to make new recordings of their own workloads with the original version of the program, and automatically generate and run the corresponding ad hoc tests on the patched version, to validate that the patch does not break functionality they rely on.

Presented by Baishakhi Ray at 2nd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages (MAPL), June 2018.

Presented by Mike Su at 24th IEEE International Conference on Program Comprehension (ICPC), May 2016.
ACM SIGSOFT Distinguished Paper Award

Presented by Mike Su at 10th International Workshop on Software Clones (IWSC), March 2016.
People’s Choice Award for Best Position Paper

Presented by Jon Bell at 10th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE), September 2015.

Presented at OSDI 2014 on October 6, 2014 in Broomfield, CO.

Presented at OOPSLA 2014 in Portland, OR on October 22, 2014

http://t.co/4qPqcQTfTw