Code translation aims to convert a program from one programming language (PL) to another. This long-standing software engineering task is crucial for modernizing legacy systems, ensuring cross-platform compatibility, enhancing performance, and more. However, automating this process remains challenging due to many syntactic and semantic differences between PLs. Recent studies show that even advanced techniques such as large language models (LLMs), especially open-source LLMs, still struggle with the task. Currently, code LLMs are trained with source code from multiple programming languages, thus presenting multilingual capabilities. In this paper, we investigate whether such capabilities can be harnessed to enhance code translation. To achieve this goal, we introduce INTERTRANS, an LLM-based automated code translation approach that, in contrast to existing approaches, leverages intermediate translations to bridge the syntactic and semantic gaps between source and target PLs. INTERTRANS contains two stages. It first utilizes a novel Tree of Code Translation (ToCT) algorithm to plan transitive intermediate translation sequences between a given source and target PL, then validates them in a specific order. We evaluate INTERTRANS with three open LLMs on three benchmarks (i.e., CodeNet, HumanEval-X, and TransCoder) involving six PLs. Results show an absolute improvement of 18.3% to 43.3% in Computation Accuracy (CA) for INTERTRANS over Direct Translation with 10 attempts. The best-performing variant of INTERTRANS (with the Magicoder LLM) achieved an average CA of 87.3%-95.4% on three benchmarks.

The pull-based development is widely adopted in modern open-source software (OSS) projects, where developers propose changes to the codebase by submitting a pull request (PR). However, due to many reasons, PRs in OSS projects frequently experience delays across their lifespan, including prolonged waiting times for the first response. Such delays may significantly impact the efficiency and productivity of the development process, as well as the retention of new contributors as long-term contributors.In this paper, we conduct an exploratory study on the time-to-first-response for PRs by analyzing 111,094 closed PRs from ten popular OSS projects on GitHub. We find that bots frequently generate the first response in a PR, and significant differences exist in the timing of bot-generated versus human-generated first responses. We then perform an empirical study to examine the characteristics of bot- and human-generated first responses, including their relationship with the PR’s lifetime. Our results suggest that the presence of bots is an important factor contributing to the time-to-first-response in the pull-based development paradigm, and hence should be separately analyzed from human responses. We also report the characteristics of PRs that are more likely to experience long waiting for the first human-generated response. Our findings have practical implications for newcomers to understand the factors contributing to delays in their PRs.