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   https://doi.org/10.1109/TNSE.2018.2837894  

   Soltan, Saleh; Yannakakis, Mihalis; Zussman, Gil (July 2019, IEEE Transactions on Network Science and Engineering)
2. React example viability for efficient API learning (REVEAL): A tool to help programmers utilize incompatible code examples in React. js

   https://doi.org/10.1016/j.cola.2023.101201  

   Allen, John; Kelleher, Caitlin. (June 2023, Journal of Computer Languages)

   Programmers typically learn APIs on-the-fly through web examples. Incompatibilities and omissions in copied example code can create barriers for these learners. We present an analysis of example usage barriers programmers faced in a previous study of React.js novices. We show that a small set of errors prevented programmers from using most found code examples. In response, we built REVEAL to detect and repair the common errors we identified in copied code. We describe the formative evaluation of REVEAL and show that REVEAL users were more likely to successfully integrate code examples than participants in the previous study. 

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3. REACT to Cyber-Physical Attacks on Power grids (Extended Abstract)

   https://doi.org/10.1145/3305218.3305238  

   Soltan, Saleh; Yannakakis, Mihalis; Zussman, Gil (January 2019, ACM SIGMETRICS Performance Evaluation Review)
4. The End-to-End Provenance Project

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5. End-to-end programmable computing systems

   https://doi.org/10.1038/s44172-023-00127-7  

   Xiao, Yao; Ma, Guixiang; Ahmed, Nesreen_K; Capotă, Mihai; Willke, Theodore_L; Nazarian, Shahin; Bogdan, Paul (November 2023, Communications Engineering)

   Abstract Recent technological advances have contributed to the rapid increase in algorithmic complexity of applications, ranging from signal processing to autonomous systems. To control this complexity and endow heterogeneous computing systems with autonomous programming and optimization capabilities, we propose aunified, end-to-end, programmable graph representation learning(PGL) framework that mines the complexity of high-level programs down to low-level virtual machine intermediate representation, extracts specific computational patterns, and predicts which code segments run best on a core in heterogeneous hardware. PGL extracts multifractal features from code graphs and exploits graph representation learning strategies for automatic parallelization and correct assignment to heterogeneous processors. The comprehensive evaluation of PGL on existing and emerging complex software demonstrates a 6.42x and 2.02x speedup compared to thread-based execution and state-of-the-art techniques, respectively. Our PGL framework leads to higher processing efficiency, which is crucial for future AI and high-performance computing applications such as autonomous vehicles and machine vision. 

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