On the naturalness of hardware descriptions

Abstract

Mining software repositories (MSR) has been shown effective for extracting data used to improve various software engineering tasks, including code completion, code repair, code search, and code summarization. Despite a large body of work on MSR, researchers have focused almost exclusively on repositories that contain code written in imperative programming languages, such as Java and C/C++. Unlike prior work, in this paper, we focus on mining publicly available hardware descriptions (HDs) written in hardware description languages (HDLs), such as VHDL. HDLs have unique syntax and semantics compared to popular imperative languages, and learningbased tools available to hardware designers are well behind those used in other application domains. We assembled large HD corpora consisting of source code written in several HDLs and report on their characteristics. Our language model evaluation reveals that HDs possess a high level of naturalness similar to software written in imperative languages. Further, by utilizing our corpora, we built several deep learning models for automated code completion in VHDL; our models take into account unique characteristics of HDLs, including similarities of nearby concurrent signal assignment statements, in-built concurrency, and the frequently used signal types. These characteristics led to more effective neural models, achieving a BLEU score of 37.3, an 8-14-point improvement over rule-based and neural baselines. CCS CONCEPTS • Hardware → Hardware description languages and compilation; • Software and its engineering → Software maintenance tools.

Learning Deep Semantics for Test Completion

“…The applications of ML models on SE tasks is an active research area in recent years. One of the most studied task is code completion, which improves developers' productivity by suggesting next tokens or statements as developers are writing code [26]- [29], [35], [54], [72]- [76]. Researchers have also studied developing ML models for other SE tasks, including code summarization [31]- [33], [47], [57], [58], [77], [78], code and comment maintenance [79]- [82], bug fixing [83]- [85], etc.…”