The Tactician (extended version): A Seamless, Interactive Tactic Learner and Prover for Coq

We present Tactician, a tactic learner and prover for the Coq Proof Assistant. Tactician helps users make tactical proof decisions while they retain control over the general proof strategy. To this end, Tactician learns from previously written tactic scripts and gives users either suggestions about the next tactic to be executed or altogether takes over the burden of proof synthesis. Tactician's goal is to provide users with a seamless, interactive, and intuitive experience together with robust and adaptive proof automation. In this paper, we give an overview of Tactician from the user's point of view, regarding both day-to-day usage and issues of package dependency management while learning in the large. Finally, we give a peek into Tactician's implementation as a Coq plugin and machine learning platform.Comment: 19 pages, 2 figures. This is an extended version of a paper published in CICM-2020. For the project website, see https://coq-tactician.github.i

Deep learning applied to the assessment of online student programming exercises

Massive online open courses (MOOCs) teaching coding are increasing in number and popularity. They commonly include homework assignments in which the students must write code that is evaluated by functional tests. Functional testing may to some extent be automated however provision of more qualitative evaluation and feedback may be prohibitively labor-intensive. Provision of qualitative evaluation at scale, automatically, is the subject of much research effort. In this thesis, deep learning is applied to the task of performing automatic assessment of source code, with a focus on provision of qualitative feedback. Four tasks: language modeling, detecting idiomatic code, semantic code search, and predicting variable names are considered in detail. First, deep learning models are applied to the task of language modeling source code. A comparison is made between the performance of different deep learning language models, and it is shown how language models can be used for source code auto-completion. It is also demonstrated how language models trained on source code can be used for transfer learning, providing improved performance on other tasks. Next, an analysis is made on how the language models from the previous task can be used to detect idiomatic code. It is shown that these language models are able to locate where a student has deviated from correct code idioms. These locations can be highlighted to the student in order to provide qualitative feedback. Then, results are shown on semantic code search, again comparing the performance across a variety of deep learning models. It is demonstrated how semantic code search can be used to reduce the time taken for qualitative evaluation, by automatically pairing a student submission with an instructor’s hand-written feedback. Finally, it is examined how deep learning can be used to predict variable names within source code. These models can be used in a qualitative evaluation setting where the deep learning models can be used to suggest more appropriate variable names. It is also shown that these models can even be used to predict the presence of functional errors. Novel experimental results show that: fine-tuning a pre-trained language model is an effective way to improve performance across a variety of tasks on source code, improving performance by 5% on average; pre-trained language models can be used as zero-shot learners across a variety of tasks, with the zero-shot performance of some architectures outperforming the fine-tuned performance of others; and that language models can be used to detect both semantic and syntactic errors. Other novel findings include: removing the non-variable tokens within source code has negligible impact on the performance of models, and that these remaining tokens can be shuffled with only a minimal decrease in performance.Engineering and Physical Sciences Research Council (EPSRC) fundin

Programming Languages and Systems

This open access book constitutes the proceedings of the 29th European Symposium on Programming, ESOP 2020, which was planned to take place in Dublin, Ireland, in April 2020, as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The actual ETAPS 2020 meeting was postponed due to the Corona pandemic. The papers deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 25th International Conference on Fundamental Approaches to Software Engineering, FASE 2022, which was held during April 4-5, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 17 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. The proceedings also contain 3 contributions from the Test-Comp Competition. The papers deal with the foundations on which software engineering is built, including topics like software engineering as an engineering discipline, requirements engineering, software architectures, software quality, model-driven development, software processes, software evolution, AI-based software engineering, and the specification, design, and implementation of particular classes of systems, such as (self-)adaptive, collaborative, AI, embedded, distributed, mobile, pervasive, cyber-physical, or service-oriented applications

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 25th International Conference on Fundamental Approaches to Software Engineering, FASE 2022, which was held during April 4-5, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 17 regular papers presented in this volume were carefully reviewed and selected from 64 submissions. The proceedings also contain 3 contributions from the Test-Comp Competition. The papers deal with the foundations on which software engineering is built, including topics like software engineering as an engineering discipline, requirements engineering, software architectures, software quality, model-driven development, software processes, software evolution, AI-based software engineering, and the specification, design, and implementation of particular classes of systems, such as (self-)adaptive, collaborative, AI, embedded, distributed, mobile, pervasive, cyber-physical, or service-oriented applications

Actes des Sixièmes journées nationales du Groupement De Recherche CNRS du Génie de la Programmation et du Logiciel

National audienceCe document contient les actes des Sixièmes journées nationales du Groupement De Recherche CNRS du Génie de la Programmation et du Logiciel (GDR GPL) s'étant déroulées au CNAM à Paris du 11 au 13 juin 2014. Les contributions présentées dans ce document ont été sélectionnées par les différents groupes de travail du GDR. Il s'agit de résumés, de nouvelles versions, de posters et de démonstrations qui correspondent à des travaux qui ont déjà été validés par les comités de programmes d'autres conférences et revues et dont les droits appartiennent exclusivement à leurs auteurs