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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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RJoules: An Energy Measurement Tool for R 23

RJoules: An Energy Measurement Tool for R

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Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering

作者： Rajrupa Chattaraj Sridhar Chimalakonda Research in Intelligent Systems and Human Analytics Lab Department of Computer Science and Engineering Indian Institute of Technology Tirupati India

ISBN: (纸本)9798350329964

With the exponential growth of data, the demand for effective data analysis tools has increased significantly. R language, known for its statistical modeling and data analysis capabilities, has become one of the most popular programming languages among data scientists and researchers. As the importance of energy-aware software systems continues to rise, several studies investigate the impact of source code and different stages of machine learning model training on energy consumption. However, existing studies in this domain primarily focus on programming languages like Python and Java, resulting in a lack of energy measuring tools for other programming languages such as R. To address this gap, we propose "RJoules", a tool designed to measure the energy consumption of R code snippets. We evaluate the correctness and performance of RJoules by applying it to four machine learning algorithms on three different systems. Our aim is to support developers and practitioners in building energy-aware systems in R. The demonstration of the tool is available at https://***/yMKFuvAM-DE and related artifacts at https://***/RJoules/.

关键词： energy measurement

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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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DBJoules: An Energy Measurement Tool for Database Management systems

arXiv

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arXiv 2023年

作者： Lella, Hemasri Sai Chattaraj, Rajrupa Manasa, Kurra Chimalakonda, Sridhar Research in Intelligent Systems and Human Analytics Lab Department of Computer Science and Engineering Indian Institute of Technology Tirupati India

In the rapidly evolving landscape of modern data-driven technologies, software relies on large datasets and constant data center operations using various database systems to support computation-intensive tasks. As energy consumption in software systems becomes a growing concern, selecting the right database from energy-efficiency perspective is also critical. To address this, we introduce DBJoules, a tool that measures the energy consumption of activities in database systems. DBJoules supports energy measurement of CRUD operations for four popular databases. Through evaluations on two widely-used datasets, we identify disparities of 7% to 38% in the energy consumption of these databases. Hence, the goal is to raise developer awareness about the effect of running queries in different databases from an energy consumption perspective, enabling them to select appropriate database for sustainable usage. The tool’s demonstration is available at https://***/D1MTZum0jok and related artifacts at https://***/DBJoules/. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy utilization

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RJoules: An Energy Measurement Tool for R

RJoules: An Energy Measurement Tool for R

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IEEE International Conference on Automated Software Engineering (ASE)

作者： Rajrupa Chattaraj Sridhar Chimalakonda Department of Computer Science and Engineering Research in Intelligent Systems and Human Analytics Lab Indian Institute of Technology Tirupati India

With the exponential growth of data, the demand for effective data analysis tools has increased significantly. R language, known for its statistical modeling and data analysis capabilities, has become one of the most popular programming languages among data scientists and researchers. As the importance of energy-aware software systems continues to rise, several studies investigate the impact of source code and different stages of machine learning model training on energy consumption. However, existing studies in this domain primarily focus on programming languages like Python and Java, resulting in a lack of energy measuring tools for other programming languages such as R. To address this gap, we propose “RJoules”, a tool designed to measure the energy consumption of R code snippets. We evaluate the correctness and performance of RJoules by applying it to four machine learning algorithms on three different systems. Our aim is to support developers and practitioners in building energy-aware systems in R. The demonstration of the tool is available at https://***/yMKFuvAM-DE and related artifacts at https://***/RJoules/.

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I Can’t Share Code, but I need Translation - An Empirical Study on Code Translation through Federated LLM

arXiv

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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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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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Flight Deck Design of a Hybrid Turbine/Electric Aircraft

Flight Deck Design of a Hybrid Turbine/Electric Aircraft

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Etherington, Timothy Ghatas, Rania W. Arthur, Trey Hill, Melissa Litt, Jonathan Crew Systems and Aviation Operations Branch Analytics Mechanics Associates HamptonVA23681 United States Human/Machine Systems Research Engineer Crew Systems and Aviation Operations Branch NASA Langley Research Center HamptonVA23681 United States Aerospace Flight Systems Branch AIAA Senior Member NASA Langley Research Center HamptonVA23681 United States Simulation Development and Analysis Branch NASA Langley Research Center HamptonVA23681 United States Intelligent Control and Autonomy Branch NASA Langley Research Center ClevelandOH44135 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The SUbsonic Single Aft eNgine (SUSAN) program is being developed as a sustainable subsonic regional aircraft. NASA is conducting trade studies for this future hybrid single aisle passenger aircraft. The goal of reducing emission levels by 50% will be enabled through an advanced design. An aft mounted single turbine engine provides thrust and electric power to drive 16 wing mounted electric engines. Using the 16 counter-rotating electric fans will create an efficient, very high bypass ratio engine. Electric engines allow for distributed thrust, reducing control surfaces and provide a greater dynamic response. Batteries provide boost for takeoff and climb, allowing a smaller, more efficient, turbine engine. As part of the SUSAN effort, two pilot-in-the-loop studies were conducted at NASA Langley research Center in Virginia to explore flight deck design issues. In particular: how many throttles are required/desired for a 17-engine aircraft, does the hybrid electric design change the answer, what are the requirements for electric engine displays, what system pages are required, and what information is required to monitor the batteries. Objective and subjective data were collected after each scenario and at the end of the day with a guided set of questionnaires from sixteen crews paired by airline. Data along with pilot comments were used to improve the throttle and engine displays between the studies and used to make the final recommendations on flight deck configurations. This paper provides final design recommendations for the SUSAN flight deck and justification for each recommendation. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Bypass ratio

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RCGraph - A Tool to Integrate Readme and Commits through Temporal Knowledge Graphs

RCGraph - A Tool to Integrate Readme and Commits through Tem...

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IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Akhila Sri Manasa Venigalla Mir Sameed Ali Nikhil M Sridhar Chimalakonda Department of Computer Science & Engineering Research in Intelligent Software & Human Analytics (RISHA) Lab Indian Institute of Technology Tirupati India

Readme files and commit logs carry important and useful project information, corresponding to project dependencies, project functionalities, additions, deletions, and so on. These two artifacts have been analysed separately to obtain project specific information corresponding to contribution guidelines, bug prediction and localisation. Linking the readme files with associated commits and further querying the linked data could help in assessing time stamp specific changes made to the readme files. Utilizing knowledge graph representation of data is observed to largely support querying and integration and extraction of data from heterogeneous sources. To this end, we present a tool to generate readme specific temporal knowledge graph, as a first step towards integrating readme files and commit logs. As commits contain temporal information of the changes, we see that overlaying this information over the corresponding text in readme files could help in arriving at a temporal knowledge graph (TKG). We present a case study of querying the TKG for one repository and further evaluate the tool on 10 repositories spanning across 10 programming languages on GitHub. For demo video, visit - https://***/4YOCDngf4bY. For website of the tool, visit - https://***/rcgraph/

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