Add two papers

Author: mallamanis

_publications/key2022speak.markdown

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+---
+layout: publication
+title: "I Speak, You Verify: Toward Trustworthy Neural Program Synthesis"
+authors: Darren Key, Wen-Ding Li, Kevin Ellis
+conference:
+year: 2022
+additional_links:
+   - {name: "ArXiV", url: "https://arxiv.org/abs/2210.00848"}
+tags: ["synthesis"]
+---
+We develop an approach for improving the trustworthiness and overall accuracy of program synthesizers based on large language models for source code. Given a natural language description of a programming problem, our method samples both candidate programs as well as candidate predicates specifying how the program should behave. We learn to analyze the agreement between programs and predicates to judge both which program is most likely to be correct, and also judge whether the language model is able to solve the programming problem in the first place. This latter capacity allows favoring high precision over broad recall: fostering trust by only proposing a program when the system is certain that it is correct.

_publications/nadeem2022codedsi.markdown

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+---
+layout: publication
+title: "CodeDSI: Differentiable Code Search"
+authors: Usama Nadeem, Noah Ziems, Shaoen Wu
+conference:
+year: 2022
+additional_links:
+   - {name: "ArXiV", url: "https://arxiv.org/abs/2210.00328"}
+tags: ["search"]
+---
+Reimplementing solutions to previously solved software engineering problems is not only inefficient but also introduces inadequate and error-prone code. Many existing methods achieve impressive performance on this issue by using autoregressive text-generation models trained on code. However, these methods are not without their flaws. The generated code from these models can be buggy, lack documentation, and introduce vulnerabilities that may go unnoticed by developers. An alternative to code generation -- neural code search -- is a field of machine learning where a model takes natural language queries as input and, in turn, relevant code samples from a database are returned. Due to the nature of this pre-existing database, code samples can be documented, tested, licensed, and checked for vulnerabilities before being used by developers in production. In this work, we present CodeDSI, an end-to-end unified approach to code search. CodeDSI is trained to directly map natural language queries to their respective code samples, which can be retrieved later. In an effort to improve the performance of code search, we have investigated docid representation strategies, impact of tokenization on docid structure, and dataset sizes on overall code search performance. Our results demonstrate CodeDSI strong performance, exceeding conventional robust baselines by 2-6% across varying dataset sizes.