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arXiv 2025年

作者： Kumar, Jahnavi Janapati, Venkata Lakshmana Sasaank Tanguturi, Mokshith Reddy Chimalakonda, Sridhar Research in Intelligent Software & Human Analytics Lab Department of Computer Science and Engineering Indian Institute of Technology Tirupati India

Owing to the rapid evolution of technologies and project requirements, organizations need to upgrade the code base in their software projects to a new version of the programming language or even translating to an entirely new one. However, code translation is resource-intensive and requires expertise in both the source and target languages. While researchers have made progress in automating translations between legacy and modern languages, recent work has increasingly turned to pre-trained Large Language Models (LLMs) to translate efficiently. Given the proprietary nature of code, organizations prefer finetuning LLMs locally rather than relying on external APIs. This is one of the first empirical studies that proposes a Federated LLM-based approach for code translation. The proposed approach enables clients to jointly train a code translator without sharing sensitive data. This study demonstrates that participants can collaboratively develop a FedLLM for efficient code translation (particularly C# to Java and vice-versa) with superior results (more than 40% improvement in CodeLLaMA’s CodeBLEU score) compared to individual client models. Our findings indicate that FedLLM offers a collaborative approach to code translation and could serve as a promising direction for future research in this field. © 2025, CC BY.

关键词： Program translators

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On the Need for Empirically Investigating Fast-Growing Programming Languages 24

On the Need for Empirically Investigating Fast-Growing Progr...

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46th International Conference on software Engineering: Companion, ICSE-Companion 2024

作者： Kumar, Jahnavi Chimalakonda, Sridhar Research in Intelligent Software & Human Analytics Lab Indian Institute of Technology Tirupati India

ISBN: (纸本)9798400705021

The research community has extensively explored various aspects of programming languages (PLs) such as developer sentiment analysis, ecosystem dynamics, pull request trends, energy consumption, and more. Existing research has predominantly focused on "Popular PLs" with the largest unique user bases which considers users since the language's inception. However, our approach shifts to examining "Fast Growing Languages" (FGLs) - those experiencing significant growth in their user base in recent years. We see this perspective as an essential exploration for understanding the current developer interest in both established and emerging languages. In this short note, we discuss (i) An empirical study on understanding how developers perceive these FGLs, analyzing their emotions through 1.86M Github comments (ii) Discuss potential future research on fast-growing PLs at the cross-section of programming languages and software engineering. © 2024 IEEE Computer Society. All rights reserved.

关键词： Energy utilization

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On the Need for Empirically Investigating Fast-Growing Programming Languages

On the Need for Empirically Investigating Fast-Growing Progr...

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International Conference on software Engineering - Companion, ICSE, Companion

作者： Jahnavi Kumar Sridhar Chimalakonda Research in Intelligent Software & Human Analytics Lab Indian Institute of Technology Tirupati India

ISBN: (数字)9798400705021

ISBN: (纸本)9798350351781

The research community has extensively explored various aspects of programming languages (PLs) such as developer sentiment analysis, ecosystem dynamics, pull request trends, energy consumption, and more. Existing research has predominantly focused on “Popular PLs” with the largest unique user bases which considers users since the language's inception. However, our approach shifts to examining “Fast Growing Languages” (FGLs) - those experiencing significant growth in their user base in recent years. We see this perspective as an essential exploration for understanding the current developer interest in both established and emerging languages. In this short note, we discuss (i) An empirical study on understanding how developers perceive these FGLs, analyzing their emotions through 1.86M Github comments (ii) Discuss potential future research on fast-growing PLs at the cross-section of programming languages and software engineering.

关键词： Computer languages Energy consumption Sentiment analysis Ecosystems Market research User experience Performance analysis

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NoteG: A Computational Notebook to Facilitate Rapid Game Prototyping

arXiv

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arXiv 2022年

作者： Mathews, Noble Saji Chimalakonda, Sridhar Research in Intelligent Software & Human Analytics Lab Indian Institute of Technology Tirupati India

Game development-based approaches are increasingly used to design curricula that can engage students, as these can help them apply and practice learnt computer science concepts. However, it can become complex to develop a minimum working game or a prototype with the help of high-end game engines. Game prototyping is one of the most essential parts of the game design and development cycle as it allows developers to continuously test and improve their ideas. In recent years, computational notebooks have gained widespread popularity among developers. They can help run individual code snippets, visualize the output, consolidate the source code, and share live code easily. However, its use has not been explored in the field of game development and prototyping. In this paper, we propose NoteG, a computational notebook towards rapid game prototyping. We evaluated the tool with 18 novice game developers through a questionnaire-based user survey. A majority of the volunteers (66%) found it easy to use and were of the opinion that it saves time. A few of the participants successfully extended the existing framework to implement new game mechanics within their prototypes. © 2022, CC BY-NC-SA.

关键词： Surveys

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SupportArr: A Plugin to Comprehend Arrays for Novice Programmers

SupportArr: A Plugin to Comprehend Arrays for Novice Program...

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International Conference on Advanced Learning Technologies (ICALT)

作者： Vartika Agrahari Sridhar Chimalakonda Research in Intelligent Software & Human Analytics (RISHA) Lab Indian Institute of Technology Tirupati India

ISBN: (数字)9781728160900

ISBN: (纸本)9781728160917

Comprehending arrays in programming is considered as one of the difficult tasks for novice programmers because of the complexity involved in their definition, declaration, and operations. Visualization of data structures and algorithms is a valuable learning aid for novice programmers. However, existing techniques show the visualization of code after compiling the program, rather than during the development process. To address this, we present SupportArr, a tool that helps in the visualization of arrays in C/C++ programming languages. The core idea is to provide an in-built IDE support for novice programmers to visualize the statements in code without executing the program. We conducted a preliminary experiment with 23 novice programmers and got a promising result from 84% of the participants.

关键词： Arrays Data visualization Programming Tools Libraries Visualization

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Refactor4Green: A Game for Novice Programmers to Learn Code Smells

Refactor4Green: A Game for Novice Programmers to Learn Code ...

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International Conference on software Engineering - Companion, ICSE, Companion

作者： Vartika Agrahari Sridhar Chimalakonda Research in Intelligent Software & Human Analytics (RISHA) Lab Indian Institute of Technology Tirupati India

ISBN: (数字)9781450371223

ISBN: (纸本)9781728165288

The rise in awareness of sustainable software has led to a focus on energy efficiency and consideration of code smells during soft-ware development. This eventually requires software engineering teachers to focus on topics such as code smells in their software engineering courses to bring awareness among students on the impact of code smells and bad design choices, not just for the soft-ware but also for the environment. Thus, we propose a desktop game named Refactor4Green to teach code smells and refactoring to novice programmers. The core idea of the game is to introduce code smells with refactoring choices through the theme of a green environment. We conducted a preliminary study with university students and got positive feedback from 83.06% of the participants.

关键词： Games software software engineering Energy efficiency Green products Tools Programming

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A Mocktail of Source Code Representations 21

A Mocktail of Source Code Representations

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IEEE International Conference on Automated software Engineering (ASE)

作者： Dheeraj Vagavolu Karthik Chandra Swarna Sridhar Chimalakonda Research in Intelligent Software & Human Analytics (RISHA) Lab Indian Institute of Technology Tirupati Tirupati India

ISBN: (纸本)9781665447843

Efficient representation of source code is essential for various software engineering tasks such as code classification and code clone detection. Most recent approaches for representing source code still use AST and do not leverage semantic graphs such as CFG and PDG. One effective technique for representing source code involves extracting paths from the AST and using a learning model to capture program properties. Code2vec is one such path-based approach that uses an attention-based neural network to learn code embeddings which can then be used for various downstream tasks. However, this approach uses only AST and does not leverage CFG and PDG. Even though an integrated graph approach (Code Property Graph) exists for representing source code, it has only been explored in the domain of software security. Moreover, it does not leverage the paths from the individual graphs. Our idea is to extend the path-based approach code2vec to include the semantic graphs CFG and PDG with AST, which is largely unexplored in software engineering. We evaluate our approach on the task of METHODNAMING using a C dataset of 730K methods collected from GitHub. In comparison to code2vec, our approach improves the F1 score by 11% on the full dataset and up to 100% with individual projects. We show that semantic features from the CFG and PDG paths drastically improve the performance of the software engineering tasks. We envision that looking at a mocktail of source code representations for various software engineering tasks can lay the foundation for a new line of research and a re-haul of existing research.

关键词： Adaptation models Codes Semantics Neural networks Syntactics software Security

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APIScanner - Towards Automated Detection of Deprecated APIs in Python Libraries

APIScanner - Towards Automated Detection of Deprecated APIs ...

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International Conference on software Engineering - Companion, ICSE, Companion

作者： Aparna Vadlamani Rishitha Kalicheti Sridhar Chimalakonda Research in Intelligent Software & Human Analytics (RISHA) Lab Indian Institute of Technology Tirupati Tirupati India

ISBN: (纸本)9781665429757

Python libraries are widely used for machine learning and scientific computing tasks today. APIs in Python libraries are deprecated due to feature enhancements and bug fixes in the same way as in other languages. These deprecated APIs are discouraged from being used in further software development. Manually detecting and replacing deprecated APIs is a tedious and time-consuming task due to the large number of API calls used in the projects. Moreover, the lack of proper documentation for these deprecated APIs makes the task challenging. To address this challenge, we propose an algorithm and a tool APIScanner that automatically detects deprecated APIs in Python libraries. This algorithm parses the source code of the libraries using abstract syntax tree (ASTs) and identifies the deprecated APIs via decorator, hard-coded warning or comments. APIScanner is a Visual Studio Code Extension that highlights and warns the developer on the use of deprecated API elements while writing the source code. The tool can help developers to avoid using deprecated API elements without the execution of code. We tested our algorithm and tool on six popular Python libraries, which detected 838 of 871 deprecated API elements. Demo of APIScanner: https://***/1hy_ugf-iek. Documentation, tool, and source code can be found here: https://***/APIScanner.

关键词： Visualization software algorithms Documentation Tools Libraries Task analysis Python

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What Do Developers Feel About Fast-Growing Programming Languages? An Exploratory Study

What Do Developers Feel About Fast-Growing Programming Langu...

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IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Jahnavi Kumar Sridhar Chimalakonda Department of Computer Science and Engineering Research in Intelligent Software & Human Analytics Lab Indian Institute of Technology Tirupati India

ISBN: (数字)9798400705861

ISBN: (纸本)9798350352276

The developer community has witnessed an unprecedented surge in recent years, with over 100 million active developers on the GitHub platform in 2023. Along with it, there is a significant rise and adoption of new programming languages, frameworks and tools. The study aims to comprehend how developers perceive these fastgrowing programming languages by performing emotion analysis of developer’s comments posted in various software artifacts such as pull requests, issues and commits of GitHub repositories. In this regard, we employed a fine-tuned small ‘Large Language Model’ (sLLM) to detect emotions, leveraging a balanced dataset from existing literature complemented with additional manual annotations from our collected data. We have analyzed 10 fast-growing programming languages, examining 1.8 million comments from 4.1 million non-code artifacts. To further validate our findings, we have performed a qualitative survey and analysis with 28 developers. Our study reveals insights into the developers emotion associated with these fast-growing languages. Notably, “Surprise’’ is the predominant emotion associated with these languages. CCS CONCEPTS • software and its engineering $\rightarrow$ General programming languages.

关键词： Surveys Computer languages Annotations software Data models Surges software development management

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Code Review Automation Via Multi-task Federated LLM - An Empirical Study

arXiv

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arXiv 2024年

作者： Kumar, Jahnavi Chimalakonda, Sridhar Research in Intelligent Software & Human Analytics Lab Department of Computer Science and Engineering Indian Institute of Technology Tirupati India

Code review is a crucial process before deploying code to production, as it validates the code, provides suggestions for improvements, and identifies errors such as missed edge cases. In projects with regular production releases, the effort required for peer code-reviews remains high. Consequently, there has been significant interest from software engineering (SE) researchers in automating the code review process. Previous research on code review automation has typically approached the task as three independent sub-tasks: review necessity prediction, review comment generation, and code refinement. Our study attempts to (i) leverage the relationships between the sub-tasks of code review automation, by developing a multi-task model that addresses all tasks in an integrated manner, and (ii) increase model robustness on unseen data via collaborative large language model (LLM) modeling, while retaining the proprietary nature of code, by using federated learning (FL). The study explores five simple techniques for multi-task training, including two sequential methods, one parallel method, and two cumulative methods. The results indicate that sequentially training a federated LLM (FedLLM) for our code review multi-task use case is less efficient in terms of time, computation, and performance metrics, compared to training separate models for each task. Because sequential training demonstrates catastrophic forgetting, alternatively cumulative fine-tuning for multi-task training performs better than training models for individual tasks. This study highlights the need for research focused on effective fine-tuning of multi-task FedLLMs for SE tasks. © 2024, CC BY.

关键词： Coding errors

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