Shuvendu Lahiri

Chenyuan Yang, Xuheng Li, Md Rakib Hossain Misu et al. · microsoft-research

Generative AI has shown its values for many software engineering tasks. Still in its infancy, large language model (LLM)-based proof generation lags behind LLM-based code generation. In this paper, we present AutoVerus. AutoVerus uses LLMs to automatically generate correctness proof for Rust code. AutoVerus is designed to match the unique features of Verus, a verification tool that can prove the correctness of Rust code using proofs and specifications also written in Rust. AutoVerus consists of a network of LLM agents that are crafted and orchestrated to mimic human experts' three phases of proof construction: preliminary proof generation, proof refinement guided by generic tips, and proof debugging guided by verification errors. To thoroughly evaluate AutoVerus and help foster future research in this direction, we have built a benchmark suite of 150 non-trivial proof tasks, based on existing code-generation benchmarks and verification benchmarks. Our evaluation shows that AutoVerus can automatically generate correct proof for more than 90% of them, with more than half of them tackled in less than 30 seconds or 3 LLM calls.

Saikat Chakraborty, Gabriel Ebner, Siddharth Bhat et al.

Proof-oriented programs mix computational content with proofs of program correctness. However, the human effort involved in programming and proving is still substantial, despite the use of Satisfiability Modulo Theories (SMT) solvers to automate proofs in languages such as F*. Seeking to spur research on using AI to automate the construction of proof-oriented programs, we curate a dataset of 600K lines of open-source F* programs and proofs, including software used in production systems ranging from Windows and Linux to Python and Firefox. Our dataset includes around 32K top-level F* definitions, each representing a type-directed program and proof synthesis problem producing a definition given a formal specification expressed as an F* type. We provide a program fragment checker that queries F* to check the correctness of candidate solutions. We also report on an extended version of our dataset containing a total of 940K lines of programs and proofs, with a total of 54k top-level F* definitions. We believe this is the largest corpus of SMT-assisted program proofs coupled with a reproducible program-fragment checker. Grounded in this dataset, we investigate the use of AI to synthesize programs and their proofs in F*, with promising results. Our main finding in that the performance of fine-tuned smaller language models (such as Phi-2 or StarCoder) compare favorably with large language models (such as GPT-4), at a much lower computational cost. We also identify various type-based retrieval augmentation techniques and find that they boost performance significantly. With detailed error analysis and case studies, we identify potential strengths and weaknesses of models and techniques and suggest directions for future improvements.

Rangeet Pan, Vu Le, Nachiappan Nagappan et al.

Forking structure is widespread in the open-source repositories and that causes a significant number of merge conflicts. In this paper, we study the problem of textual merge conflicts from the perspective of Microsoft Edge, a large, highly collaborative fork off the main Chromium branch with significant merge conflicts. Broadly, this study is divided into two sections. First, we empirically evaluate textual merge conflicts in Microsoft Edge and classify them based on the type of files, location of conflicts in a file, and the size of conflicts. We found that ~28% of the merge conflicts are 1-2 line changes, and many resolutions have frequent patterns. Second, driven by these findings, we explore Program Synthesis (for the first time) to learn patterns and resolve structural merge conflicts. We propose a novel domain-specific language (DSL) that captures many of the repetitive merge conflict resolution patterns and learn resolution strategies as programs in this DSL from example resolutions. We found that the learned strategies can resolve 11.4% of the conflicts (~41% of 1-2 line changes) that arise in the C++ files with 93.2% accuracy.

Chuyue Sun, Yican Sun, Daneshvar Amrollahi et al.

We introduce VeriStruct, a novel framework that extends AI-assisted automated verification from single functions to more complex data structure modules in Verus. VeriStruct employs a planner module to orchestrate the systematic generation of abstractions, type invariants, specifications, and proof code. To address the challenge that LLMs often misunderstand Verus' annotation syntax and verification-specific semantics, VeriStruct embeds syntax guidance within prompts and includes a repair stage to automatically correct annotation errors. In an evaluation on eleven Rust data structure modules, VeriStruct succeeds on ten of the eleven, successfully verifying 128 out of 129 functions (99.2%) in total. These results represent an important step toward the goal of automatic AI-assisted formal verification.

Saikat Dutta, Diego Garbervetsky, Shuvendu Lahiri et al.

Static analysis has established itself as a weapon of choice for detecting security vulnerabilities. Taint analysis in particular is a very general and powerful technique, where security policies are expressed in terms of forbidden flows, either from untrusted input sources to sensitive sinks (in integrity policies) or from sensitive sources to untrusted sinks (in confidentiality policies). The appeal of this approach is that the taint-tracking mechanism has to be implemented only once, and can then be parameterized with different taint specifications (that is, sets of sources and sinks, as well as any sanitizers that render otherwise problematic flows innocuous) to detect many different kinds of vulnerabilities. But while techniques for implementing scalable inter-procedural static taint tracking are fairly well established, crafting taint specifications is still more of an art than a science, and in practice tends to involve a lot of manual effort. Past work has focussed on automated techniques for inferring taint specifications for libraries either from their implementation or from the way they tend to be used in client code. Among the latter, machine learning-based approaches have shown great promise. In this work we present our experience combining an existing machine-learning approach to mining sink specifications for JavaScript libraries with manual taint modelling in the context of GitHub's CodeQL analysis framework. We show that the machine-learning component can successfully infer many new taint sinks that either are not part of the manual modelling or are not detected due to analysis incompleteness. Moreover, we present techniques for organizing sink predictions using automated ranking and code-similarity metrics that allow an analysis engineer to efficiently sift through large numbers of predictions to identify true positives.

Alexey Svyatkovskiy, Sarah Fakhoury, Negar Ghorbani et al.

Collaborative software development is an integral part of the modern software development life cycle, essential to the success of large-scale software projects. When multiple developers make concurrent changes around the same lines of code, a merge conflict may occur. Such conflicts stall pull requests and continuous integration pipelines for hours to several days, seriously hurting developer productivity. To address this problem, we introduce MergeBERT, a novel neural program merge framework based on token-level three-way differencing and a transformer encoder model. By exploiting the restricted nature of merge conflict resolutions, we reformulate the task of generating the resolution sequence as a classification task over a set of primitive merge patterns extracted from real-world merge commit data. Our model achieves 63-68% accuracy for merge resolution synthesis, yielding nearly a 3x performance improvement over existing semi-structured, and 2x improvement over neural program merge tools. Finally, we demonstrate that MergeBERT is sufficiently flexible to work with source code files in Java, JavaScript, TypeScript, and C# programming languages. To measure the practical use of MergeBERT, we conduct a user study to evaluate MergeBERT suggestions with 25 developers from large OSS projects on 122 real-world conflicts they encountered. Results suggest that in practice, MergeBERT resolutions would be accepted at a higher rate than estimated by automatic metrics for precision and accuracy. Additionally, we use participant feedback to identify future avenues for improvement of MergeBERT.

Chungha Sung, Shuvendu Lahiri, Constantin Enea et al.

When an evolving program is modified to address issues related to thread synchronization, there is a need to confirm the change is correct, i.e., it does not introduce unexpected behavior. However, manually comparing two programs to identify the semantic difference is labor intensive and error prone, whereas techniques based on model checking are computationally expensive. To fill the gap, we develop a fast and approximate static analysis for computing synchronization differences of two programs. The method is fast because, instead of relying on heavy-weight model checking techniques, it leverages a polynomial-time Datalog-based program analysis framework to compute differentiating data-flow edges, i.e., edges allowed by one program but not the other. Although approximation is used our method is sufficiently accurate due to careful design of the Datalog inference rules and iterative increase of the required data-flow edges for representing a difference. We have implemented our method and evaluated it on a large number of multithreaded C programs to confirm its ability to produce, often within seconds, the same differences obtained by human; in contrast, prior techniques based on model checking take minutes or even hours and thus can be 10x to 1000x slower.