Overcoming Language Dichotomies: Toward Effective Program Comprehension for Mobile App Development

Mobile devices and platforms have become an established target for modern software developers due to performant hardware and a large and growing user base numbering in the billions. Despite their popularity, the software development process for mobile apps comes with a set of unique, domain-specific challenges rooted in program comprehension. Many of these challenges stem from developer difficulties in reasoning about different representations of a program, a phenomenon we define as a "language dichotomy". In this paper, we reflect upon the various language dichotomies that contribute to open problems in program comprehension and development for mobile apps. Furthermore, to help guide the research community towards effective solutions for these problems, we provide a roadmap of directions for future work.Comment: Invited Keynote Paper for the 26th IEEE/ACM International Conference on Program Comprehension (ICPC'18

Large Language Models for Software Engineering: A Systematic Literature Review

Large Language Models (LLMs) have significantly impacted numerous domains, notably including Software Engineering (SE). Nevertheless, a well-rounded understanding of the application, effects, and possible limitations of LLMs within SE is still in its early stages. To bridge this gap, our systematic literature review takes a deep dive into the intersection of LLMs and SE, with a particular focus on understanding how LLMs can be exploited in SE to optimize processes and outcomes. Through a comprehensive review approach, we collect and analyze a total of 229 research papers from 2017 to 2023 to answer four key research questions (RQs). In RQ1, we categorize and provide a comparative analysis of different LLMs that have been employed in SE tasks, laying out their distinctive features and uses. For RQ2, we detail the methods involved in data collection, preprocessing, and application in this realm, shedding light on the critical role of robust, well-curated datasets for successful LLM implementation. RQ3 allows us to examine the specific SE tasks where LLMs have shown remarkable success, illuminating their practical contributions to the field. Finally, RQ4 investigates the strategies employed to optimize and evaluate the performance of LLMs in SE, as well as the common techniques related to prompt optimization. Armed with insights drawn from addressing the aforementioned RQs, we sketch a picture of the current state-of-the-art, pinpointing trends, identifying gaps in existing research, and flagging promising areas for future study

Addressing the evolution of automated user behaviour patterns by runtime model interpretation

The final publication is available at Springer via http://dx.doi.org/10.1007/s10270-013-0371-3The use of high-level abstraction models can facilitate and improve not only system development but also runtime system evolution. This is the idea of this work, in which behavioural models created at design time are also used at runtime to evolve system behaviour. These behavioural models describe the routine tasks that users want to be automated by the system. However, users¿ needs may change after system deployment, and the routine tasks automated by the system must evolve to adapt to these changes. To facilitate this evolution, the automation of the specified routine tasks is achieved by directly interpreting the models at runtime. This turns models into the primary means to understand and interact with the system behaviour associated with the routine tasks as well as to execute and modify it. Thus, we provide tools to allow the adaptation of this behaviour by modifying the models at runtime. This means that the system behaviour evolution is performed by using high-level abstractions and avoiding the costs and risks associated with shutting down and restarting the system.This work has been developed with the support of MICINN, under the project EVERYWARE TIN2010-18011, and the support of the Christian Doppler Forschungsgesellschaft and the BMWFJ, Austria.Serral Asensio, E.; Valderas Aranda, PJ.; Pelechano Ferragud, V. (2013). Addressing the evolution of automated user behaviour patterns by runtime model interpretation. Software and Systems Modeling. https://doi.org/10.1007/s10270-013-0371-3SWeiser, M.: The computer of the 21st century. Sci. Am. 265, 66–75 (1991)Serral, E., Valderas, P., Pelechano, V.: Context-adaptive coordination of pervasive services by interpreting models during runtime. Comput. 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A web-based AI assistant Application using Python and JavaScript

Our research is mainly based on a chatbot which is powered by Artificial Intelligence. Nowadays, Artificial Intelligence assistants such as Apple’s Siri, Google’s Now and Amazon’s Alexa are currently fast-growing and widely integrated with many smart devices. These assistants are built with the primary purpose of being personal assistants for every individual user in certain contexts. In this research, we would highlight the development process of the chatbots, features, problems, case studies and limitations. This research delivers the information, helps developers to build answer bots and integrate chatbots with business accounts. The aim is to assist users and allow transactions between client companies and their customers. As a result, users can accomplish results to queries as well as clients can grow their business

Chatbots for Modelling, Modelling of Chatbots

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 28-03-202

ChatGPT is on the Horizon: Could a Large Language Model be Suitable for Intelligent Traffic Safety Research and Applications?

ChatGPT embarks on a new era of artificial intelligence and will revolutionize the way we approach intelligent traffic safety systems. This paper begins with a brief introduction about the development of large language models (LLMs). Next, we exemplify using ChatGPT to address key traffic safety issues. Furthermore, we discuss the controversies surrounding LLMs, raise critical questions for their deployment, and provide our solutions. Moreover, we propose an idea of multi-modality representation learning for smarter traffic safety decision-making and open more questions for application improvement. We believe that LLM will both shape and potentially facilitate components of traffic safety research.Comment: Submitted to Nature - Machine Intelligence (Revised and Extended

How to Data in Datathons

The rise of datathons, also known as data or data science hackathons, has provided a platform to collaborate, learn, and innovate in a short timeframe. Despite their significant potential benefits, organizations often struggle to effectively work with data due to a lack of clear guidelines and best practices for potential issues that might arise. Drawing on our own experiences and insights from organizing >80 datathon challenges with >60 partnership organizations since 2016, we provide guidelines and recommendations that serve as a resource for organizers to navigate the data-related complexities of datathons. We apply our proposed framework to 10 case studies.Comment: 37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmar

Scalable and Quality-Aware Training Data Acquisition for Conversational Cognitive Services

Dialog Systems (or simply bots) have recently become a popular human-computer interface for performing user's tasks, by invoking the appropriate back-end APIs (Application Programming Interfaces) based on the user's request in natural language. Building task-oriented bots, which aim at performing real-world tasks (e.g., booking flights), has become feasible with the continuous advances in Natural Language Processing (NLP), Artificial Intelligence (AI), and the countless number of devices which allow third-party software systems to invoke their back-end APIs. Nonetheless, bot development technologies are still in their preliminary stages, with several unsolved theoretical and technical challenges stemming from the ambiguous nature of human languages. Given the richness of natural language, supervised models require a large number of user utterances paired with their corresponding tasks -- called intents. To build a bot, developers need to manually translate APIs to utterances (called canonical utterances) and paraphrase them to obtain a diverse set of utterances. Crowdsourcing has been widely used to obtain such datasets, by paraphrasing the initial utterances generated by the bot developers for each task. However, there are several unsolved issues. First, generating canonical utterances requires manual efforts, making bot development both expensive and hard to scale. Second, since crowd workers may be anonymous and are asked to provide open-ended text (paraphrases), crowdsourced paraphrases may be noisy and incorrect (not conveying the same intent as the given task). This thesis first surveys the state-of-the-art approaches for collecting large training utterances for task-oriented bots. Next, we conduct an empirical study to identify quality issues of crowdsourced utterances (e.g., grammatical errors, semantic completeness). Moreover, we propose novel approaches for identifying unqualified crowd workers and eliminating malicious workers from crowdsourcing tasks. Particularly, we propose a novel technique to promote the diversity of crowdsourced paraphrases by dynamically generating word suggestions while crowd workers are paraphrasing a particular utterance. Moreover, we propose a novel technique to automatically translate APIs to canonical utterances. Finally, we present our platform to automatically generate bots out of API specifications. We also conduct thorough experiments to validate the proposed techniques and models