Main TrackAIware 2024

Accepted Papers

	Title
	A Case Study of LLM for Automated Vulnerability Repair: Assessing Impact of Reasoning and Patch Validation Feedback Main Track Ummay Kulsum, Haotian Zhu, Bowen Xu, Marcelo d'Amorim DOI
	A Comparative Analysis of Large Language Models for Code Documentation Generation Main Track Shubhang Shekhar Dvivedi, Vyshnav Vijay, Sai Leela Rahul Pujari, Shoumik Lodh, Dhruv Kumar DOI
	AI-Assisted Assessment of Coding Practices in Modern Code Review Main Track Manushree Vijayvergiya, Malgorzata Salawa, Ivan Budiselic, Dan Zheng, Pascal Lamblin, Marko Ivanković, Juanjo Carin, Mateusz Lewko, Jovan Andonov, Goran Petrović, Danny Tarlow, Petros Maniatis, René Just DOI
	An AI System Evaluation Framework for Advancing AI Safety: Terminology, Taxonomy, Lifecycle Mapping Main Track Boming Xia, Qinghua Lu, Liming Zhu, Zhenchang Xing DOI
	A Transformer-Based Approach for Smart Invocation of Automatic Code Completion Main Track Aral de Moor, Arie van Deursen, Maliheh Izadi DOI
	Automatic Programming vs. Artificial Intelligence Main Track James Noble DOI
	Chain of Targeted Verification Questions to Improve the Reliability of Code Generated by LLMs Main Track Sylvain Kouemo Ngassom, Arghavan Moradi Dakhel, Florian Tambon, Foutse Khomh DOI
	Effectiveness of ChatGPT for Static Analysis: How Far Are We? Main Track Mohammad Mahdi Mohajer, Reem Aleithan, Nima Shiri Harzevili, Moshi Wei, Alvine Boaye Belle, Hung Viet Pham, Song Wang DOI
	From Human-to-Human to Human-to-Bot Conversations in Software Engineering Main Track Ranim Khojah, Francisco Gomes de Oliveira Neto, Philipp Leitner DOI Pre-print
	Function+Data Flow: A Framework to Specify Machine Learning Pipelines for Digital Twinning Main Track Eduardo de Conto, Blaise Genest, Arvind Easwaran DOI Pre-print
	Green AI in Action: Strategic Model Selection for Ensembles in Production Main Track Nienke Nijkamp, June Sallou, Niels van der Heijden, Luís Cruz DOI Pre-print
	Identifying the Factors That Influence Trust in AI Code Completion Main Track Adam Brown, Sarah D'Angelo, Ambar Murillo, Ciera Jaspan, Collin Green DOI
	Leveraging Machine Learning for Optimal Object-Relational Database Mapping in Software Systems Main Track Sasan Azizian, Elham Rastegari, Hamid Bagheri DOI
	Measuring Impacts of Poisoning on Model Parameters and Embeddings for Large Language Models of Code Main Track Aftab Hussain, Md Rafiqul Islam Rabin, Amin Alipour DOI
	Neuro-Symbolic Approach to Certified Scientific Software Synthesis Main Track Hamid Bagheri, Mehdi Mirakhorli, Mohamad Fazelnia, Ibrahim Mujhid, Md Rashedul Hasan DOI
	RUBICON: Rubric-Based Evaluation of Domain-Specific Human AI Conversations Main Track Param Biyani, Yasharth Bajpai, Arjun Radhakrishna, Gustavo Soares, Sumit Gulwani DOI
	SolMover: Smart Contract Code Translation Based on Concepts Main Track Rabimba Karanjai, Lei Xu, Weidong Shi DOI
	The Art of Programming: Challenges in Generating Code for Creative Applications Main Track Michael Cook DOI
	The Role of Generative AI in Software Development Productivity: A Pilot Case Study Main Track Mariana Coutinho, Lorena Marques, Anderson Santos, Marcio Dahia, Cesar França, Ronnie de Souza Santos DOI
	Towards AI for Software Systems Main Track Nafise Eskandani, Guido Salvaneschi DOI
	Unveiling Assumptions: Exploring the Decisions of AI Chatbots and Human Testers Main Track Francisco Gomes de Oliveira Neto DOI
	Unveiling the Potential of a Conversational Agent in Developer Support: Insights from Mozilla’s PDF.js Project Main Track João Correia, Morgan C. Nicholson, Daniel Coutinho, Caio Barbosa, Marco Castelluccio, Marco Gerosa, Alessandro Garcia, Igor Steinmacher DOI Pre-print

Call for Papers

“Software for all and by all” is the future of humanity. AIware, i.e., AI-powered software, has the potential to democratize software creation. We must reimagine software and software engineering (SE), enabling individuals of all backgrounds to participate in its creation with higher reliability and quality. Over the past decade, software has evolved from human-driven Codeware to the first generation of AIware, known as Neuralware, developed by AI experts. Foundation Models (FMs, including Large Language Models or LLMs), like GPT, ushered in software’s next generation, Promptware, led by domain and prompt experts. However, this merely scratches the surface of the future of software. We are already witnessing the emergence of the next generation of software, Agentware, in which humans and intelligent agents jointly lead the creation of software. With the advent of brain-like World Models and brain-computer interfaces, we anticipate the arrival of Mindware, representing another generation of software. Agentware and Mindware promise greater autonomy and widespread accessibility, with non-expert individuals, known as Software Makers, offering oversight to autonomous agents.

The software engineering community will need to develop fundamentally new approaches and evolve existing ones, so they are suitable for a world in which software creation is within the reach of Software Makers of all levels of SE expertise, as opposed to solely expert developers. We must recognize a shift in where expertise lies in software creation and start making the needed changes in the type of research that is being conducted, the ways that SE is being taught, and the support that is offered to software makers.

The 1st ACM International Conference on AI-powered Software (AIware 2024, https://2024.aiwareconf.org/) will be hosted on July 15th-16th, 2024, at Porto de Galinhas, Brazil, co-located with FSE’24. AIware 2024 aims to bring the software engineering community together in anticipation of the upcoming changes driven by FMs and look at them from the perspective of AI-powered software and their evolution. AIware 2024 promotes cross-disciplinary discussions, identifies emerging research challenges, and establishes a new research agenda for the community in the Foundation Model era.

Topics of interest

Topics of interest of AIware conference include but are not limited to the following:

• How would future software look like in the FM era?
• How to integrate legacy software in future AIware?
• Do existing programming models (e.g., object-oriented or functional programming) and SE practices (e.g., test-driven development and agile) remain suitable for developing and maintaining software in the FM era?
• What roles do autonomous agents play in the development and maintenance of software in the FM era?
• How will inner and open source collaboration evolve in the FM era?
• What kind of release engineering practices do we need for FM-powered software applications? Are LLMOps comprehensive enough to capture the release engineering needs of AIware in the FM era?
• How do we debug and monitor AIware in the FM era?
• How should we change SE curriculum, training and mentoring in the FM era?
• How to evolve FMs from the perspective of AIware and its makers in the FM era?
• How do human interactions and perceptions shape the development and implementation of AIware in the FM era?
• How do we measure and improve the trustworthiness of AIware in the FM era?
• What are the implications and effectiveness of foundation models in improving software engineering practices and outcomes?
• How does AIware impact developer productivity?

Types of submissions

AIware 2024 Main Track welcomes submissions from both academia and industry. At least one author from each accepted submission will be required to attend the conference and present the paper. Submissions can include but are not limited to: case studies, vision papers, literature surveys, position papers, theoretical, and applied research papers.

Page limits:

• Case studies, literature surveys, theoretical, applied research papers: 6 - 8 pages;
• Vision papers, position papers: 2 - 4 pages;

With an additional 1-2 pages of reference. The page limit is strict.

Submission guidelines

All authors should use the official “ACM Primary Article Template”, as can be obtained from the ACM Proceedings Template page. LaTeX users should use the following latex code at the start of the LaTeX document where the review option produces line numbers for easy reference by the reviewers and the anonymous optician omits author names:

\documentclass[sigconf,review,anonymous]{acmart}

Papers must be submitted electronically through the following submission site: https://aiware24.hotcrp.com/.

All submissions must be in PDF. All papers must be written in English.

All submissions are subject to ACM policies including ACM Publications Policies, ACM’s new Publications Policy on Research Involving Human Participants and Subjects, ACM Policy and Procedures on Plagiarism, ACM Policy on Prior Publication and Simultaneous Submissions, and the ACM Policy on Authorship and its accompanying FAQ released April 20, 2023. In particular, authors should pay attention to the following points:

• Generative AI tools and technologies, such as ChatGPT, may not be listed as authors of an ACM published Work. The use of generative AI tools and technologies to create content is permitted but must be fully disclosed in the Work. For example, the authors could include the following statement in the Acknowledgements section of the Work: ChatGPT was used to generate sections of this Work, including text, tables, graphs, code, data, citations, etc. If you are uncertain about the need to disclose the use of a particular tool, err on the side of caution, and include a disclosure in the acknowledgements section of the Work.
• If you are using generative AI software tools to edit and improve the quality of your existing text in much the same way you would use a typing assistant like Grammarly to improve spelling, grammar, punctuation, clarity, engagement or to use a basic word processing system to correct spelling or grammar, it is not necessary to disclose such usage of these tools in your Work.

Review and evaluation process

A double-anonymous review process will be employed for submissions to the main track. The submission must not reveal the identity of the authors in any way. Papers that violate the double-anonymous requirement will be desk-rejected. For more details on the double-anonymous process, please refer to FSE’s double-anonymous review process.

All submissions will be desk-checked to make sure that they are within the scope of the conference and have satisfied the submission requirements (e.g., page limits and anonymity). Three members of the Program Committee will then be assigned for each submission for the review process. The Program Committee members can bid on submissions to review. The Program Committee will discuss the review results virtually and decide on the accepted submissions. The accepted submissions will be published in the ACM digital library.

AUTHORS TAKE NOTE: The official publication date is the date the proceedings are made available in the ACM Digital Library. This date may be up to two weeks prior to the first day of the conference. The official publication date affects the deadline for any patent filings related to published work.

Awards

The best full-length papers accepted in the main track of AIware will be recognized with an ACM SIGSOFT Distinguished Paper Awards.

In addition, selected AIware papers will be invited to be revised and extended for consideration in a special issue of the Empirical Software Engineering journal by Springer.

Important dates

All dates are 23:59:59 AoE (UTC-12h).

• Intent submission (optional): Mar 22, 2024
• Papers submission: Mar 29th, 2024
• Papers notification: April 26th, 2024
• Papers camera-ready: May 17th, 2024
• Conference dates: July 15th-16th, 2024

Notes

We are aware that the event dates for AIware and 2030 Software Engineering are conflicting. The organizers of the two events are coordinating the events’ programs, such that authors will have the opportunity to benefit from and participate in both. While a given paper can only be submitted to one of these events, the organizers, at their discretion, will align both events’ programs in order to allow cross-pollination between both communities.