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1. Dozer: Migrating Shell Commands to Ansible Modules via Execution Profiling and Synthesis

   Horton, Eric; Parnin, Chris (January 2022, Proceedings of the International Conference on Software Engineering)

   Software developers frequently use the system shell to perform configuration management tasks. Unfortunately, the shell does not scale well to large systems, and configuration management tools like Ansible are more difficult to learn. We address this problem with Dozer, a technique to help developers push their shell commands into Ansible task definitions. It operates by tracing and comparing system calls to find Ansible modules with similar behaviors to shell commands, then generating and validating migrations to find the task which produces the most similar changes to the system. Dozer is syntax agnostic, which should allow it to generalize to other configuration management platforms. We evaluate Dozer using datasets from open source configuration scripts. 

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2. APPINITE: A Multi-Modal Interface for Specifying Data Descriptions in Programming by Demonstration Using Natural Language Instructions

   https://doi.org/10.1109/VLHCC.2018.8506506  

   Li, Toby Jia-Jun; Labutov, Igor; Li, Xiaohan Nancy; Zhang, Xiaoyi; Shi, Wenze; Ding, Wanling; Mitchell, Tom M.; Myers, Brad A. (October 2018, 2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC'18))

   A key challenge for generalizing programming-by-demonstration (PBD) scripts is the data description problem - when a user demonstrates performing an action, the system needs to determine features for describing this action and the target object in a way that can reflect the user's intention for the action. However, prior approaches for creating data descriptions in PBD systems have problems with usability, applicability, feasibility, transparency and/or user control. Our APPINITE system introduces a multimodal interface with which users can specify data descriptions verbally using natural language instructions. APPINITE guides users to describe their intentions for the demonstrated actions through mixed-initiative conversations. APPINITE constructs data descriptions for these actions from the natural language instructions. Our evaluation showed that APPINITE is easy-to-use and effective in creating scripts for tasks that would otherwise be difficult to create with prior PBD systems, due to ambiguous data descriptions in demonstrations on GUIs. 

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3. Automated Synthesis of Access Control Lists

   Liu, Xiao; Holden, Brett; Wu, Dinghao (July 2017, Proceedings of the 3rd International Conference on Software Security and Assurance (ICSSA 2017))

   Network configuration remains time-consuming and error-prone with the current configuration command system. To create access control lists (ACLs) with commands containing many options is still considered as a difficult task. In light of this, we aim to develop a comprehensible way to the ACL con- struction. Based on Eliza, a prototype of Artificial Intelligence, we propose a new design called EASYACL that synthesizes ACL rules automatically from natural language descriptions. E A S YAC L demonstrates the effectiveness of domain-specific program synthesis. Through the use of natural language, ACL rules can be constructed without using an excessive number of options or rigid syntax. By introducing the batch processing, we make it possible for users to apply configurations to a range of IP addresses rather than tediously repeating commands. EASYACL supports multi-platform by an intermediate repre- sentation which may be ported to the commands for both Cisco and Juniper devices. The comprehensible commands are friendly for encapsulation as well as reuse. E A S YAC L enables end-users with no prior programming experience to construct ACL in a natural way which lowers the bar for security management training and also reduces the errors in network administration. 

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4. Automatic generation of interactive NPC scripts for a mixed-reality integrated learning environment

   Yuan, A.; Ke, F.; Naglieri, R.; Dai, Z.; Pachman, M. (October 2018, the International Conference Proceedings Series by ACM, 2018 10th International Conference on Education Technology and Computers (ICETC 2018) Proceedings Series by ACM (ISBN: 978-1-4503-6517-8))

   The Mixed-Reality Integrated Learning Environment (MILE) developed at Florida State University is a virtual reality based, inclusive and immersive e-learning environment that promotes engaging and effective learning interactions for a diversified learner population. MILE uses a large number of interactive Non-Player Characters (NPCs) to represent diverse research-based learner archetypes and groups, and to prompt and provide feedback for in situ teaching practice. The NPC scripts in MILE are written in Linden Scripting Language (LSL), and can be quite complex, creating a significant challenge in the development and maintenance of the system. To address this challenge, we develop NPC_GEN, an automatic NPC script generation tool that takes high-level NPC descriptions as input and automatically produces LSL scripts for NPCs. In this work, we introduce NPCDL, a language that we design for NPC_GEN to give high-level descriptions of NPCs, describe how NPC_GEN translates an NPCDL description into an LSL script, and report a user study of NPC_GEN. The results of our user study indicate that with minimal training, non-technical people are able to write and modify NPCDL descriptions, which can then be used to generate LSL scripts for the NPCs: the development and maintenance of NPCs is greatly simplified with NPC_GEN. 

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5. SceneCraft: Automating Interactive Narrative Scene Generation in Digital Games with Large Language Models

   https://doi.org/10.1609/aiide.v19i1.27504  

   Kumaran, Vikram; Rowe, Jonathan; Mott, Bradford; Lester, James (October 2023, Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment)

   Creating engaging interactive story-based experiences dynamically responding to individual player choices poses significant challenges for narrative-centered games. Recent advances in pre-trained large language models (LLMs) have the potential to revolutionize procedural content generation for narrative-centered games. Historically, interactive narrative generation has specified pivotal events in the storyline, often utilizing planning-based approaches toward achieving narrative coherence and maintaining the story arc. However, manual authorship is typically used to create detail and variety in non-player character (NPC) interaction to specify and instantiate plot events. This paper proposes SCENECRAFT, a narrative scene generation framework that automates NPC interaction crucial to unfolding plot events. SCENECRAFT interprets natural language instructions about scene objectives, NPC traits, location, and narrative variations. It then employs large language models to generate game scenes aligned with authorial intent. It generates branching conversation paths that adapt to player choices while adhering to the author’s interaction goals. LLMs generate interaction scripts, semantically extract character emotions and gestures to align with the script, and convert dialogues into a game scripting language. The generated script can then be played utilizing an existing narrative-centered game framework. Through empirical evaluation using automated and human assessments, we demonstrate SCENECRAFT’s effectiveness in creating narrative experiences based on creativity, adaptability, and alignment with intended author instructions. 

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