Three pillars, one mission: keeping software systems secure, reliable, and understandable at scale.
Machine learning and LLM-driven approaches for Software Operations.
Analytics and empirical methods for Software Operations.
Build systems, CI/CD, IaC, security analysis, dependency management.
From the lab
Aniruddhan Murali, Noble Saji Mathews, Mahmoud Alfadel, Meng Xu, Meiyappan Nagappan
We present SnipTest, a technique that uses fuzzing on multi-level code slices to validate vulnerabilities. SnipTest uncovered 19 new vulnerabilities in heavily used open-source projects such as OpenSSL and libpcap, leading to one confirmed CVE-2025-11964.
Read morePartha Chakraborty, Mahmoud Alfadel, Meiyappan Nagappan
Software bugs require developers to expend significant effort to identify and resolve them, often consuming about one-third of their time. Bug localization, the process of pinpointing the exact source code files that need modification, is crucial in reducing this effort. We propose BLAZE, an approach that employs dynamic chunking and hard example learning to overcome limitations of existing techniques in cross-project and multi-language environments.
Read moreAniruddhan Murali, Noble Saji Mathews, Mahmoud Alfadel, Meiyappan Nagappan, Meng Xu
We present FuzzSlice, a technique that uses function-level fuzzing to prune false positives from static analysis warnings, helping developers focus on findings that are more likely to represent real vulnerabilities. FuzzSlice was integrated by the security company Chainguard into their toolchain.
Read moreLocated at
in Calgary, Alberta — one of Canada's most dynamic and rapidly growing cities. The LASSO Lab is part of the Faculty of Science
Latest from the group
Aug 2026
PublicationOur paper "SnipTest: Fuzzing Multi-Level Code Slices for Validating Vulnerabilities" has been accepted at IEEE Transactions on Software Engineering (TSE).
Feb 2026
PublicationOur work on linking declined proposals to source code and on Policy as Code has been accepted at MSR 2026.