Caroline Lemieux



Caroline Lemieux

PhD Candidate
University of California Berkeley
Department of Computer Science
Email: clemieuxbots-gotta-get-smarter-than-this@its-really-not-that-hardcs.berkeley.edu
Twitter: @cestlemieux
Github: carolemieux

About

I am a fourth year Ph.D. candidate at the University of California, Berkeley, advised by Koushik Sen. My research interests centre around improving, or helping developers to improve, the correctness, reliability, and understanding of software systems. I am particularly interested in developing automated methods for engineering tasks such as testing, debugging, and comprehension, and lean towards work with strong empirical aspects (i.e. requiring large-scale data analysis).

This interest is a defining thread through my current and past research projects. My current research into automated testing, particularly fuzz testing, aims to produce tools which test programs as effectively as possible with as little user input as possible. In Summer 2018, I interned at Google, where I built large-scale static analysis to automatically generate fuzz targets. In Summer 2017 I was a research intern in the Tools for Software Engineers group at Microsoft, working on automating detection of anomolous errors in the distributed build system CloudBuild. At UBC, I worked with Ivan Beschastnikh on automated specification mining tools.

In May 2016 I received my B.Sc. (in Combined Honours Computer Science and Mathematics) at the University of British Columbia, and was awarded the Governor General's Silver Medal for highest standing in the graduating class of the Faculty of Science.

I was awarded a Google PhD Fellowship in Programming Technologies and Software Engineering for 2019.

Projects


Current

AutoPandas Modern Python APIs are complex and very difficult to learn. Novice users could ask their API questions on StackOverflow, but the answer might be slow to arrive or unpersonalized... enter AutoPandas. AutoPandas is a programming-by-example synthesis engine for the Python API pandas, in particular for its dataframe transformations. To handle the complex space of API programs, AutoPandas uses a novel neural-backed generator approach to synthesizing programs in the pandas API. You can try out AutoPandas live at autopandas.io.
FuzzFactory FuzzFactory is an extension of AFL that generalizes coverage-guided fuzzing to domain-specific testing goals. FuzzFactory allows users to guide the fuzzer's search process without having to modify the core search algorithm. FuzzFactory's key abstraction is that of waypoints: intermediate inputs that are saved during the fuzzing loop. For example, PerfFuzz saves inputs that increase loop execution counts, a magic-byte fuzzer may save inputs that have partially correct magic bytes, or a directed fuzzer may save inputs that are more likely to exercise a program point of interest.
Zest Binary-level mutational fuzzing excels at exercising the syntactic (parsing) phase of programs, but produces few valid inputs that exercise deeper stages of the program. QuickCheck-style random testing allows us to test programs with random generators of highly-structured inputs, but does not use program feedback to bias its input generation. Zest, the default front-end of our JQF platform, leverages (1) QuickCheck-style generators to generate only syntactically valid inputs, and (2) program coverage and validity feedback to generate inputs which explore deep parts of the program.
PerfFuzz Performance problems in software can arise unexpectedly when programs are provided with inputs that exhibit pathological behavior. But how can we find these inputs in the first place? Given a program and at least one seed input, PerfFuzz automatically generates inputs that exercise pathological behavior across program locations, without any domain knowledge.
FairFuzz FairFuzz is a fuzzer built on top of AFL which targets rare branches to achieve faster program coverage. FairFuzz achieves this by (1) selectively mutating inputs which exercise branches hit by few fuzz-tester generated inputs and (2) using a mutation mask to restrict mutations of these inputs to the parts which can be mutated while still hitting the branch of interest. On our benchmarks, FairFuzz achieves program coverage than AFL or AFLFast, and has a particular advantage on programs with highly nested structure.

Past

Texada I am the main developer of the Texada tool, which mines linear temporal logic (LTL) relationships of arbitrary length and complexity from textual logs. Texada takes as input a log of traces and a property type expressed in LTL and outputs instantiations of this property types with log events which hold on the entire log. Texada also supports confidence and support thresholds to allow for mining on imperfect or incomplete logs.
Quarry I also built the Quarry tool. Quarry interfaces data predicates with temporal invariants in order to extract data-temporal invariants of arbitrary length and complexity from program execution. Quarry mines relationships between Daikon-style data predicates specified in linear temporal logic (LTL). Quarry uses Daikon for data predicate inference and Texada for inference of temporal invariants.
Introduction to Systematic Program Design I worked on one of UBC's first computer science MOOCs, Introduction to Systematic Program Design, while the course was still hosted on Coursera. I worked on both MOOC offerings, focusing mostly on video lecture development, but also participating in TA tasks like replying to students on forums and composing peer-graded projects. The video lectures from the Coursera offering were used in CPSC 110, UBC's introductory computer science course, and are available for viewing here.

Publications

Posters

Mentoring

I've been lucky to work with some great students while at Berkeley.

Service

Funding

I am thankful to have received funding from Google, NSERC, NSF, UBC, and UCB to support my research.