program_transformation.md

Program Transformation

• Automated Program Refinement: Guide and Verify Code Large Language Model with Refinement Calculus, (POPL2025)

  • Abstract: Recently, the rise of code-centric large language models (LLMs) appears to have reshaped the software engineering world with low-barrier tools like Copilot that can generate code easily. However, there is no correctness guarantee for the code generated by LLMs, which suffer from the hallucination problem, and their output is fraught with risks. Besides, the end-to-end process from specification to code through LLMs is a non-transparent and uncontrolled black box. This opacity makes it difficult ...
  • Labels: code generation, program transformation, static analysis, program verification

• Automated Validation of COBOL to Java Transformation, (ASE2024)

  • Abstract: Recent advances in Large Language Model (LLM) based Generative AI techniques have made it feasible to translate enterpriselevel code from legacy languages such as COBOL to modern languages such as Java or Python. While the results of LLM-based automatic transformation are encouraging, the resulting code cannot be trusted to correctly translate the original code. We propose a framework and a tool to help validate the equivalence of COBOL and translated Java. The results can also help repair the c...
  • Labels: code generation, program transformation

• Bridging Gaps in LLM Code Translation: Reducing Errors with Call Graphs and Bridged Debuggers, (ASE2024)

  • Abstract: When using large language models (LLMs) for code translation of complex software, numerous compilation and runtime errors can occur due to insufficient context awareness. To address this issue, this paper presents a code translation method based on call graphs and bridged debuggers: TransGraph. TransGraph first obtains the call graph of the entire code project using the Language Server Protocol, which provides a detailed description of the function call relationships in the program. Through this...
  • Labels: code generation, program transformation

• Concrat: An Automatic C-to-Rust Lock API Translator for Concurrent Programs, (ICSE2023)

  • Abstract: Concurrent programs suffer from data races. To prevent data races, programmers use locks. However, programs can eliminate data races only when they acquire and release correct locks at correct timing. The lock API of C, in which people have developed a large portion of legacy system programs, does not validate the correct use of locks. On the other hand, Rust, a recently developed system programming language, provides a lock API that guarantees the correct use of locks via type checking. This ma...
  • Labels: code generation, program transformation

• Context-aware Code Segmentation for C-to-Rust Translation using Large Language Models, (arXiv2024)

  • Abstract: There is strong motivation to translate C code into Rust code due to the continuing threat of memory safety vulnerabilities in existing C programs and the significant attention paid to Rust as an alternative to the C language. While large language models (LLMs) show promise for automating this translation by generating more natural and safer code than rule-based methods, previous studies have shown that LLM-generated Rust code often fails to compile, even for relatively small C programs, due to ...
  • Labels: code generation, program transformation

• Domain-specific transformer models for query translation, (ACL2023)

  • Abstract: Due to the democratization of e-commerce, many product companies are listing their goods for online shopping. For periodic buying within a domain such as Grocery, consumers are generally inclined to buy certain brands of products. Due to a large non-English speaking population in India, we observe a significant percentage of code-mix Hinglish search queries e.g., sasta atta. An intuitive approach to dealing with code-mix queries is to train an encoder-decoder model to translate the query to Engl...
  • Labels: code generation, program transformation

• Enabling Memory Safety of C Programs using LLMs, (arXiv2024)

  • Abstract: Memory safety violations in low-level code, written in languages like C, continues to remain one of the major sources of software vulnerabilities. One method of removing such violations by construction is to port C code to a safe C dialect. Such dialects rely on programmer-supplied annotations to guarantee safety with minimal runtime overhead. This porting, however, is a manual process that imposes significant burden on the programmer and, hence, there has been limited adoption of this technique...
  • Labels: code generation, program transformation

• Exploring and Unleashing the Power of Large Language Models in Automated Code Translation, (FSE2024)

  • Abstract: Code translation tools, namely transpilers, are developed for automatic source-to-source translation. Latest learning-based transpilers have shown impressive enhancement against rule-based counterparts in both translation accuracy and readability, owing to their task-specific pre-training on extensive monolingual corpora. Nevertheless, their current performance still remains unsatisfactory for practical deployment, and the associated training resources are also prohibitively expensive. Large Lan...
  • Labels: code generation, program transformation, code model, code model training, empirical study

• Guess & Sketch: Language Model Guided Transpilation, (ICLR2024)

  • Abstract: Maintaining legacy software requires many software and systems engineering hours. Assembly code programs, which demand low-level control over the computer machine state and have no variable names, are particularly difficult for humans to analyze. Existing conventional program translators guarantee correctness, but are hand-engineered for the source and target programming languages in question. Learned transpilation, i.e. automatic translation of code, offers an alternative to manual re-writing a...
  • Labels: code generation, program transformation

• HECS: A Hypergraph Learning-Based System for Detecting Extract Class Refactoring Opportunities, (ISSTA2024)

  • Abstract: HECS is an advanced tool designed for Extract Class refactoring by leveraging hypergraph learning to model complex dependencies within large classes. Unlike traditional tools that rely on direct one-to-one dependency graphs, HECS uses intra-class dependency hypergraphs to capture one-to-many relationships. This allows HECS to provide more accurate and relevant refactoring suggestions. The tool constructs hypergraphs for each target class, attributes nodes using a pre-trained code model, and trai...
  • Labels: code generation, program transformation

• Introducing Compiler Semantics into Large Language Models as Programming Language Translators: A Case Study of C to x86 Assembly, (EMNLP2024)

  • Abstract: Compilers are complex software containing millions of lines of code, taking years to develop. This paper investigates to what extent Large Language Models (LLMs) can replace hand-crafted compilers in translating high-level programming languages to machine instructions, using C to x86 assembly as a case study. We identify two challenges of using LLMs for code translation and introduce two novel data pre-processing techniques to address the challenges: numerical value conversion and training data ...
  • Labels: code generation, program transformation

• LLM-Based Java Concurrent Program to ArkTS Converter, (ASE2024)

  • Abstract: HarmonyOS NEXT is a distributed operating system developed to support HarmonyOS native apps. To support the new and independent Harmony ecosystem, developers are required to migrate their applications from Android to HarmonyOS. However, HarmonyOS utilizes ArkTS, a superset of TypeScript, as the programming language for application development. Hence, migrating applications to HarmonyOS requires translating programs across different program languages, e.g., Java, which is known to be very challen...
  • Labels: code generation, program transformation

• LPR: Large Language Models-Aided Program Reduction, (ISSTA2024)

  • Abstract: Program reduction is a widely used technique to facilitate debugging compilers by automatically minimizing programs that trigger compiler bugs. Existing program reduction techniques are either generic to a wide range of languages (such as Perses and Vulcan) or specifically optimized for one certain language by exploiting language-specific knowledge (e.g., C-Reduce). However, synergistically combining both g...
  • Labels: code generation, program transformation, program testing, debugging

• Learning performance-improving code edits, (ICLR2024)

  • Abstract: With the waning of Moore's law, optimizing program performance has become a major focus of software research. However, high-level optimizations such as API and algorithm changes remain elusive due to the difficulty of understanding the semantics of code. Simultaneously, pretrained large language models (LLMs) have demonstrated strong capabilities at solving a wide range of programming tasks. To that end, we introduce a framework for adapting LLMs to high-level program optimization. First, we cur...
  • Labels: code generation, program transformation

• Lost in Translation: A Study of Bugs Introduced by Large Language Models while Translating Code, (ICSE2024)

  • Abstract: Code translation aims to convert source code from one programming language (PL) to another. Given the promising abilities of large language models (LLMs) in code synthesis, researchers are exploring their potential to automate code translation. The prerequisite for advancing the state of LLM-based code translation is to understand their promises and limitations over existing techniques. To that end, we present a large-scale empirical study to investigate the ability of general LLMs and code LLMs...
  • Labels: code generation, program transformation, empirical study

• Multilingual Code Co-evolution using Large Language Models, (FSE2023)

  • Abstract: Many software projects implement APIs and algorithms in multiple programming languages. Maintaining such projects is tiresome, as developers have to ensure that any change (e.g., a bug fix or a new feature) is being propagated, timely and without errors, to implementations in other programming languages. In the world of ever-changing software, using rule-based translation tools (i.e., transpilers) or machine learning models for translating code from one language to another provides limited value...
  • Labels: code generation, program transformation, code model, code model training, source code model

• One-to-One or One-to-Many? Suggesting Extract Class Refactoring Opportunities with Intra-class Dependency Hypergraph Neural Network, (ISSTA2024)

  • Abstract: Excessively large classes that encapsulate multiple responsibilities are challenging to comprehend and maintain. Addressing this issue, several Extract Class refactoring tools have been proposed, employing a two-phase process: identifying suitable fields or methods for extraction, and implementing the mechanics of refactoring. These tools traditionally generate an intra-class dependency graph to analyze the class structure, applying hard-coded rules based on this graph to unearth refactoring opp...
  • Labels: code generation, program transformation

• Rectifier: Code translation with corrector via llms, (arXiv2024)

  • Abstract: Software migration is garnering increasing attention with the evolution of software and society. Early studies mainly relied on handcrafted translation rules to translate between two languages, the translation process is error-prone and time-consuming. In recent years, researchers have begun to explore the use of pre-trained large language models (LLMs) in code translation. However, code translation is a complex task that LLMs would generate mistakes during code translation, they all produce cer...
  • Labels: code generation, program transformation

• Refactoring programs using large language models with few-shot examples, (APSEC2023)

  • Abstract: A less complex and more straightforward program is a crucial factor that enhances its maintainability and makes writing secure and bug-free programs easier. However, due to its heavy workload and the risks of breaking the working programs, programmers are reluctant to do code refactoring, and thus, it also causes the loss of potential learning experiences. To mitigate this, we demonstrate the application of using a large language model (LLM), GPT-3.5, to suggest less complex versions of the user...
  • Labels: code generation, program transformation

• Refactoring to Pythonic Idioms: A Hybrid Knowledge-Driven Approach Leveraging Large Language Models, (FSE2024)

  • Abstract: Pythonic idioms are highly valued and widely used in the Python programming community. However, many Python users find it challenging to use Pythonic idioms. Adopting rule-based approach or LLM-only approach is not sufficient to overcome three persistent challenges of code idiomatization including code miss, wrong detection and wrong refactoring. Motivated by the determinism of rules and adaptability of LLMs, we propose a hybrid approach consisting of three modules. We not only write prompts to...
  • Labels: code generation, program transformation

• Rethinking Code Refinement: Learning to Judge Code Efficiency, (EMNLP2024)

  • Abstract: Large Language Models (LLMs) have demonstrated impressive capabilities in understanding and generating codes. Due to these capabilities, many recent methods are proposed to automatically refine the codes with LLMs. However, we should rethink that the refined codes (from LLMs and even humans) are not always more efficient than their original versions. On the other hand, running two different versions of codes and comparing them every time is not ideal and time-consuming. Therefore, in this work, ...
  • Labels: code generation, program transformation, code model, code model training, source code model

• Rust-lancet: Automated Ownership-Rule-Violation Fixing with Behavior Preservation, (ICSE2024)

  • Abstract: As a relatively new programming language, Rust is designed to provide both memory safety and runtime performance. To achieve this goal, Rust conducts rigorous static checks against its safety rules during compilation, effectively eliminating memory safety issues that plague C/C++ programs. Although useful, the safety rules pose programming challenges to Rust programmers, since programmers can easily violate safety rules when coding in Rust, leading their code to be rejected by the Rust compiler,...
  • Labels: code generation, program transformation

• Semi-Supervised Code Translation Overcoming the Scarcity of Parallel Code Data, (ASE2024)

  • Abstract: Neural code translation is the task of converting source code from one programming language to another. One of the main challenges is the scarcity of parallel code data, which hinders the ability of translation models to learn accurate cross-language alignments. In this paper, we introduce MIRACLE, a semi-supervised approach that improves code translation through synthesizing high-quality parallel code data and curriculum learning on code data with ascending alignment levels. MIRACLE leverages s...
  • Labels: code generation, program transformation

• Three Heads Are Better Than One: Suggesting Move Method Refactoring Opportunities with Inter-class Code Entity Dependency Enhanced Hybrid Hypergraph Neural Network, (ASE2024)

  • Abstract: Methods implemented in incorrect classes will cause excessive reliance on other classes than their own, known as a typical code smell symptom: feature envy, which makes it difficult to maintain increased coupling between classes. Addressing this issue, several Move Method refactoring tools have been proposed, employing a two-phase process: identifying misplaced methods to move and appropriate classes to receive, and implementing the mechanics of refactoring. These tools traditionally use hard-co...
  • Labels: code generation, program transformation

• Towards Translating Real-World Code with LLMs: A Study of Translating to Rust, (arXiv2024)

  • Abstract: Large language models (LLMs) show promise in code translation - the task of translating code written in one programming language to another language - due to their ability to write code in most programming languages. However, LLM's effectiveness on translating real-world code remains largely unstudied. In this work, we perform the first substantial study on LLM-based translation to Rust by assessing the ability of five state-of-the-art LLMs, GPT4, Claude 3, Claude 2.1, Gemini Pro, and Mixtral. W...
  • Labels: code generation, program transformation, program testing, fuzzing

• TransLLaMa: LLM-based Simultaneous Translation System, (EMNLP2024)

  • Abstract: Decoder-only large language models (LLMs) have recently demonstrated impressive capabilities in text generation and reasoning. Nonetheless, they have limited applications in simultaneous machine translation (SiMT), currently dominated by encoder-decoder transformers. This study demonstrates that, after fine-tuning on a small dataset comprising causally aligned source and target sentence pairs, a pre-trained open-source LLM can control input segmentation directly by generating a special “wait” to...
  • Labels: code generation, program transformation, empirical study

• Unprecedented Code Change Automation: The Fusion of LLMs and Transformation by Example, (FSE2024)

  • Abstract: Software developers often repeat the same code changes within a project or across different projects. These repetitive changes are known as “code change patterns” (CPATs). Automating CPATs is crucial to expedite the software development process. While current Transformation by Example (TBE) techniques can automate CPATs, they are limited by the quality and quantity of the provided input examples. Thus, they miss transforming code variations that do not have the exact syntax, data-, or control-fl...
  • Labels: code generation, program transformation

• VERT: Verified Equivalent Rust Transpilation with Large Language Models as Few-Shot Learners, (arXiv2024)

  • Abstract: Rust is a programming language that combines memory safety and low-level control, providing C-like performance while guaranteeing the absence of undefined behaviors by default. Rust's growing popularity has prompted research on safe and correct transpiling of existing code-bases to Rust. Existing work falls into two categories: rule-based and large language model (LLM)-based. While rule-based approaches can theoretically produce correct transpilations that maintain input-output equivalence to th...
  • Labels: code generation, program transformation, static analysis, program verification

• Virtual Compiler Is All You Need For Assembly Code Search, (ACL2024)

  • Abstract: Assembly code search is vital for reducing the burden on reverse engineers, allowing them to quickly identify specific functions using natural language within vast binary programs.Despite its significance, this critical task is impeded by the complexities involved in building high-quality datasets. This paper explores training a Large Language Model (LLM) to emulate a general compiler. By leveraging Ubuntu packages to compile a dataset of 20 billion tokens, we further continue pre-train CodeLlam...
  • Labels: code generation, program transformation, static analysis, code search, code model, code model training, source code model