Cobol2Vec: Learning Representations of Cobol code (2201.09448v1)

Abstract: There has been a steadily growing interest in development of novel methods to learn a representation of a given input data and subsequently using them for several downstream tasks. The field of natural language processing has seen a significant improvement in different tasks by incorporating pre-trained embeddings into their pipelines. Recently, these methods have been applied to programming languages with a view to improve developer productivity. In this paper, we present an unsupervised learning approach to encode old mainframe languages into a fixed dimensional vector space. We use COBOL as our motivating example and create a corpus and demonstrate the efficacy of our approach in a code-retrieval task on our corpus.

• The paper introduces an unsupervised seq2seq autoencoder with a bi-directional LSTM to capture the semantics of COBOL code.
• The paper transforms verbose COBOL syntax into fixed-dimensional embeddings by normalizing tokens and employing an attention mechanism.
• The paper demonstrates that these embeddings enhance code retrieval and provide a foundational step for automated legacy system migration.

Analysis of "Cobol2Vec: Learning Representation of COBOL code"

The research paper "Cobol2Vec: Learning Representation of COBOL code" by Ankit Kulshrestha and Vishwas Lele delineates an approach for encoding COBOL, a legacy programming language, into a fixed-dimensional vector space. The paper’s central focus is to develop an unsupervised learning framework that effectively captures the semantics and structural qualities of COBOL code, facilitating enhanced code retrieval.

Objective and Motivation

COBOL, while an antiquated programming language, still supports a multitude of legacy systems that are costly to maintain. Recognizing the challenge of migrating obsolete programming languages to modern alternatives, the paper pursues an important step in this direction – learning comprehensive representations of COBOL code. While deep learning approaches have been previously successful in rendering meaning from natural languages and popular high-level languages such as Java, the intrinsic complexity and verbosity of COBOL present unique hurdles that this paper aims to address.

Methodological Framework

The authors introduce a novel abstract structure representation for COBOL codes, emphasizing their preference for sequence-based over structural or path-based representations typical in Abstract Syntax Trees (AST). This representation reduces noise by mapping user-defined variables and identifiers to special tokens, counteracting COBOL's verbose nature. Importantly, the paper leverages a sequence-to-sequence (seq2seq) autoencoder architecture to learn these representations, exploiting a bi-directional LSTM to model the inherent forward and backward dependencies present in COBOL programming constructs.

Experimental Setup and Results

Utilizing an internally generated COBOL dataset comprising approximately 11,000 sentences, the model is trained to minimize the negative log-likelihood of the reconstructed sequences. Augmenting the seq2seq model with an attention mechanism was noted as beneficial, likely due to the inherent structural dependencies within COBOL code. The model’s performance is exemplified via code retrieval tasks, where embeddings are utilized to discover semantically similar code snippets. The usage of UMAP for visualizing clusters of code embeddings provides qualitative insights into the structure of the COBOL codebase.

Implications and Future Potentials

The implications of this research are twofold: by providing a cornerstone methodology for encoding COBOL into vectors, it aids in deciphering legacy systems, and it sets the stage for further advancements in automated code migration. The authors hint at future research directions, including the refinement of context-sensitive feature engineering in programming languages different from those the methodologies were initially designed for.

Moreover, the methodology raises pertinent discussions on the scalability and adaptability of similar deep learning models for other mainframe or esoteric languages, potentially extending to include various industry-specific languages that have been deprecated over time due to the evolution of programming paradigms.

The research establishes a foundational step towards the integration of older codebases into modern development processes while proposing a robust architecture adaptable to diverse coding languages. Further exploration may substantiate these findings, potentially leading to efficient system modernization tools that could be pivotal in reducing operational costs and maintaining legacy systems.

In conclusion, the paper presents a methodologically sound paper paving the way for using deep learning techniques in interpreting and transforming legacy programming languages, with COBOL as the initial focus, while suggesting a promising trajectory for future inquiries into the domain of code representation learning.