Add missing pubs.

Author: mallamanis

_publications/chen2022codet.markdown

@@ -6,6 +6,6 @@ conference:
 year: 2022
 additional_links:
    - {name: "ArXiV", url: "https://arxiv.org/abs/2207.10397"}
-tags: ["synthesis"]
+tags: ["synthesis", "Transformer", "execution"]
 ---
 Given a programming problem, pre-trained language models such as Codex have demonstrated the ability to generate multiple different code solutions via sampling. However, selecting a correct or best solution from those samples still remains a challenge. While an easy way to verify the correctness of a code solution is through executing test cases, producing high-quality test cases is prohibitively expensive. In this paper, we explore the use of pre-trained language models to automatically generate test cases, calling our method CodeT: Code generation with generated Tests. CodeT executes the code solutions using the generated test cases, and then chooses the best solution based on a dual execution agreement with both the generated test cases and other generated solutions. We evaluate CodeT on five different pre-trained models with both HumanEval and MBPP benchmarks. Extensive experimental results demonstrate CodeT can achieve significant, consistent, and surprising improvements over previous methods. For example, CodeT improves the pass@1 on HumanEval to 65.8%, an increase of absolute 18.8% on the code-davinci-002 model, and an absolute 20+% improvement over previous state-of-the-art results.

_publications/zlotchevski2022exploring.markdown

@@ -0,0 +1,11 @@
+---
+layout: publication
+title: "Exploring and Evaluating Personalized Models for Code Generation"
+authors: Andrei Zlotchevski, Dawn Drain, Alexey Svyatkovskiy, Colin Clement, Neel Sundaresan, Michele Tufano
+conference: FSE
+year: 2022
+additional_links:
+   - {name: "ArXiV", url: "https://arxiv.org/abs/2208.13928"}
+tags: ["Transformer"]
+---
+Large Transformer models achieved the state-of-the-art status for Natural Language Understanding tasks and are increasingly becoming the baseline model architecture for modeling source code. Transformers are usually pre-trained on large unsupervised corpora, learning token representations and transformations relevant to modeling generally available text, and are then fine-tuned on a particular downstream task of interest. While fine-tuning is a tried-and-true method for adapting a model to a new domain -- for example, question-answering on a given topic -- generalization remains an on-going challenge. In this paper, we explore and evaluate transformer model fine-tuning for personalization. In the context of generating unit tests for Java methods, we evaluate learning to personalize to a specific software project using several personalization techniques. We consider three key approaches: (i) custom fine-tuning, which allows all the model parameters to be tuned; (ii) lightweight fine-tuning, which freezes most of the model's parameters, allowing tuning of the token embeddings and softmax layer only or the final layer alone; (iii) prefix tuning, which keeps model parameters frozen, but optimizes a small project-specific prefix vector. Each of these techniques offers a trade-off in total compute cost and predictive performance, which we evaluate by code and task-specific metrics, training time, and total computational operations. We compare these fine-tuning strategies for code generation and discuss the potential generalization and cost benefits of each in various deployment scenarios.