Publications

You can also find my articles on my Google Scholar profile.

Conference Papers

Speculative automated refactoring of imperative Deep Learning programs to graph execution

Published in 40th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2025

We present an automated refactoring approach that assists developers in determining which otherwise eagerly-executed imperative DL functions could be effectively and efficiently executed as graphs.

Citation: Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, and Anita Raja. Speculative automated refactoring of imperative Deep Learning programs to graph execution. In International Conference on Automated Software Engineering, ASE ’25. IEEE/ACM, IEEE, November 2025. (245/1190; 20.6% acceptance rate). To appear.
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Towards safe automated refactoring of imperative Deep Learning programs to graph execution.

Published in NIER track of the IEEE/ACM International Conference on Automated Software Engineering (ASE), 2023

We present our ongoing work on automated refactoring that assists developers in specifying whether and how their otherwise eagerly-executed imperative DL code could be reliably and efficiently executed as graphs while preserving semantics.

Citation: Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, and Anita Raja. Towards safe automated refactoring of imperative Deep Learning programs to graph execution. In International Conference on Automated Software Engineering, ASE ’23, pages 1800–1802. IEEE, September 2023. NIER track. (25/70; 35.7% acceptance rate).
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Challenges in migrating imperative Deep Learning programs to graph execution: An empirical study.

Published in 2022 International Conference on Mining Software Repositories (MSR), 2022

We conduct a data-driven analysis of challenges—and resultant bugs—involved in writing reliable yet performant imperative DL code by studying 250 open-source projects, consisting of 19.7 MLOC, along with 470 and 446 manually examined code patches and bug reports, respectively.

Citation: Tatiana Castro Vélez, Raffi Khatchadourian, Mehdi Bagherzadeh, and Anita Raja. Challenges in migrating imperative Deep Learning programs to graph execution: An empirical study. In International Conference on Mining Software Repositories, MSR ’22, pages 469–481, New York, NY, USA, May 2022. IEEE/ACM, ACM. (45/138; 32.6% acceptance rate).
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Tool Papers

Hybridize Functions: A tool for automatically refactoring imperative Deep Learning programs to graph execution.

Published in Artur Boronat and Gordon Fraser, editors, Fundamental Approaches to Software Engineering (FASE), 2025

We discuss the engineering aspects of a refactoring tool that automatically determines when it is safe and potentially advantageous to migrate imperative DL code to graph execution and vice-versa.

Citation: Raffi Khatchadourian, Tatiana Castro Vélez, Mehdi Bagherzadeh, Nan Jia, and Anita Raja. Hybridize Functions: A tool for automatically refactoring imperative Deep Learning programs to graph execution. In Artur Boronat and Gordon Fraser, editors, Fundamental Approaches to Software Engineering, FASE ’25, pages 89–100, Cham, May 2025. ETAPS, Springer Nature Switzerland. (11/31; 35% acceptance rate). EAPLS Distinguished Paper Award.
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Thesis

Towards Automated Evolution of Imperative Deep Learning Programs

Published in PhD thesis, City University of New York (CUNY) Graduate Center, 365 5th Ave, New York, NY 10016, 2025

This dissertation addresses a significant knowledge gap in understanding the practical application of hybridization in real-world DL systems. Without these insights, DL systems risk inefficiency, fragility, and high maintenance costs. This work presents an in-depth analysis of hybridization, focusing on its challenges, evolution, and usage patterns, and offers actionable recommendations, best practices, and anti-patterns for developers. Additionally, I develop an automated refactoring tool to analyze DL program source code, assess the suitability of hybridization, and optimize its application.

Citation: Tatiana Castro Vélez. Towards Automated Evolution of Imperative Deep Learning Programs. PhD thesis, City University of New York (CUNY) Graduate Center, 365 5th Ave, New York, NY 10016, September 2025.
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