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A Survey of Reinforcement Learning for Software Engineering
Dong Wang, Hanmo You, Lingwei Zhu, Kaiwei Lin, Zheng Chen, Chen Yang, Junji Yu, Zan Wang, Junjie Chen
https://arxiv.org/abs/2507.12483

A Survey of Reinforcement Learning for Software Engineering
Reinforcement Learning (RL) has emerged as a powerful paradigm for sequential decision-making and has attracted growing interest across various domains, particularly following the advent of Deep Reinforcement Learning (DRL) in 2015. Simultaneously, the rapid advancement of Large Language Models (LLMs) has further fueled interest in integrating RL with LLMs to enable more adaptive and intelligent systems. In the field of software engineering (SE), the increasing complexity of systems and the ris…

Lattice Mismatch Driven In Plane Strain Engineering for Enhanced Upper Critical Fields in Mo2N Superconducting Thin Films
Aditya Singh, Divya Rawat, Victor Hjort, Abhisek Mishra, Arnaud le Febvrier, Subhankar Bedanta, Per Eklund, Ajay Soni
https://arxiv.org/abs/2506.04750

Lattice Mismatch Driven In Plane Strain Engineering for Enhanced Upper Critical Fields in Mo2N Superconducting Thin Films
Transition metal nitrides are a fascinating class of hard coating material that provide an excellent platform for investigating superconductivity and fundamental electron phonon interactions. In this work the structural morphological and superconducting properties have been studied for Mo2N thin films deposited via direct current magnetron sputtering on cplane Al2O3 and MgO substrates to elucidate the effect of internal strain on superconducting properties. High resolution X Ray diffraction and…