Harald C. Gall
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View article: Multi Language Models for On-the-Fly Syntax Highlighting
Multi Language Models for On-the-Fly Syntax Highlighting Open
Syntax highlighting is a critical feature in modern software development environments, enhancing code readability and developer productivity. However, delivering accurate highlighting in real time remains challenging for online and web-bas…
View article: Anchor Attention, Small Cache: Code Generation with Large Language Models
Anchor Attention, Small Cache: Code Generation with Large Language Models Open
The development of large language models (LLMs) has revolutionized automated code generation. However, their high demand of computation resources has hindered a broader deployment and raised environmental concerns. A common strategy for di…
View article: Toward granular search-based automatic unit test case generation
Toward granular search-based automatic unit test case generation Open
Unit testing verifies the presence of faults in individual software components. Previous research has been targeting the automatic generation of unit tests through the adoption of random or search-based algorithms. Despite their effectiven…
View article: Trustworthy Distributed Certification of Program Execution
Trustworthy Distributed Certification of Program Execution Open
Verifying the execution of a program is complicated and often limited by the inability to validate the code's correctness. It is a crucial aspect of scientific research, where it is needed to ensure the reproducibility and validity of expe…
View article: On-the-Fly Syntax Highlighting Generalisation and Speed-ups - Replication Package
On-the-Fly Syntax Highlighting Generalisation and Speed-ups - Replication Package Open
This replication package contains both the extended syntax highlighting dataset and the software needed to reproduce the study presented in the paper On-the-Fly Syntax Highlighting Generalisability and Speed-ups. It can be reused for futur…
View article: DRIVE: Dockerfile Rule Mining and Violation Detection
DRIVE: Dockerfile Rule Mining and Violation Detection Open
A Dockerfile defines a set of instructions to build Docker images, which can then be instantiated to support containerized applications. Recent studies have revealed a considerable amount of quality issues with Dockerfiles. In this paper, …
View article: Towards Top-Down Automated Development in Limited Scopes: A Neuro-Symbolic Framework from Expressibles to Executables
Towards Top-Down Automated Development in Limited Scopes: A Neuro-Symbolic Framework from Expressibles to Executables Open
Deep code generation is a topic of deep learning for software engineering\n(DL4SE), which adopts neural models to generate code for the intended\nfunctions. Since end-to-end neural methods lack domain knowledge and software\nhierarchy awar…
View article: On-the-fly syntax highlighting using neural networks
On-the-fly syntax highlighting using neural networks Open
With the presence of online collaborative tools for software developers,\nsource code is shared and consulted frequently, from code viewers to merge\nrequests and code snippets. Typically, code highlighting quality in such\nscenarios is sa…
View article: Continuous Deep Learning: A Workflow to Bring Models into Production
Continuous Deep Learning: A Workflow to Bring Models into Production Open
Researchers have been highly active to investigate the classical machine learning workflow and integrate best practices from the software engineering lifecycle. However, deep learning exhibits deviations that are not yet covered in this co…
View article: Synthetic End-User Testing: Modeling Realistic Agents Based on Behavioral Examples
Synthetic End-User Testing: Modeling Realistic Agents Based on Behavioral Examples Open
For software interacting directly with real-world end-users, it is common practice to script scenario tests validating the system's compliance with a number of its features. However, these do not accommodate the replication of the type of …
View article: On-the-Fly Syntax Highlighting Using Neural Networks - Replication Package (Data)
On-the-Fly Syntax Highlighting Using Neural Networks - Replication Package (Data) Open
This dataset includes the data to replicate the study for the paper On-the-Fly Syntax Highlighting Using Neural Networks. It can be reused for future research in the field. We also include the detailed results obtained by executing our app…
View article: On-the-Fly Syntax Highlighting Using Neural Networks - Replication Package (Data)
On-the-Fly Syntax Highlighting Using Neural Networks - Replication Package (Data) Open
This dataset includes the data to replicate the study for the paper On-the-Fly Syntax Highlighting Using Neural Networks. It can be reused for future research in the field. We also include the detailed results obtained by executing our app…
View article: On the Effectiveness of Transfer Learning for Code Search
On the Effectiveness of Transfer Learning for Code Search Open
The Transformer architecture and transfer learning have marked a quantum leap\nin natural language processing, improving the state of the art across a range\nof text-based tasks. This paper examines how these advancements can be applied\nt…
View article: On the Effectiveness of Transfer Learning for Code Search - Replication Package
On the Effectiveness of Transfer Learning for Code Search - Replication Package Open
This repository represents the replication package for the paper On the Effectiveness of Transfer Learning for Code Search. The paper is published in the journal IEEE Transactions on Software Engineering (TSE). In this replication package,…
View article: On the Effectiveness of Transfer Learning for Code Search - Replication Package
On the Effectiveness of Transfer Learning for Code Search - Replication Package Open
This repository represents the replication package for the paper On the Effectiveness of Transfer Learning for Code Search. The paper is published in the journal IEEE Transactions on Software Engineering (TSE). In this replication package,…
View article: Toward Granular Automatic Unit Test Case Generation
Toward Granular Automatic Unit Test Case Generation Open
Unit testing verifies the presence of faults in individual software components. Previous research has been targeting the automatic generation of unit tests through the adoption of random or search-based algorithms. Despite their effectiven…
View article: Assemble Foundation Models for Automatic Code Summarization
Assemble Foundation Models for Automatic Code Summarization Open
Automatic code summarization is beneficial to daily software development since it could help reduce the requirement of manual writing. Currently, artificial intelligence is undergoing a paradigm shift. The foundation models pretrained on m…
View article: Replication Package "Applying Test Case Prioritization to Software Microbenchmarks"
Replication Package "Applying Test Case Prioritization to Software Microbenchmarks" Open
Replication package for the paper "Applying Test Case Prioritization to Software Microbenchmarks" accepted for publication in Empirical Software Engineering.
View article: Replication Package "Applying Test Case Prioritization to Software Microbenchmarks"
Replication Package "Applying Test Case Prioritization to Software Microbenchmarks" Open
Replication package for the paper "Applying Test Case Prioritization to Software Microbenchmarks" accepted for publication in Empirical Software Engineering.
View article: On the Effectiveness of Transfer Learning for Code Search
On the Effectiveness of Transfer Learning for Code Search Open
The Transformer architecture and transfer learning have marked a quantum leap in natural language processing, improving the state of the art across a range of text-based tasks. This paper examines how these advancements can be applied to a…
View article: Adversarial Robustness of Deep Code Comment Generation
Adversarial Robustness of Deep Code Comment Generation Open
Deep neural networks (DNNs) have shown remarkable performance in a variety of domains such as computer vision, speech recognition, or natural language processing. Recently they also have been applied to various software engineering tasks, …
View article: Boosting API Recommendation With Implicit Feedback
Boosting API Recommendation With Implicit Feedback Open
Developers often need to use appropriate APIs to program efficiently, but it is usually a difficult task to identify the exact one they need from a vast of candidates. To ease the burden, a multitude of API recommendation approaches have b…
View article: Dynamically reconfiguring software microbenchmarks: reducing execution time without sacrificing result quality
Dynamically reconfiguring software microbenchmarks: reducing execution time without sacrificing result quality Open
Executing software microbenchmarks, a form of small-scale performance tests predominantly used for libraries and frameworks, is a costly endeavor. Full benchmark suites take up to multiple hours or days to execute, rendering frequent check…
View article: Configuration smells in continuous delivery pipelines: a linter and a six-month study on GitLab
Configuration smells in continuous delivery pipelines: a linter and a six-month study on GitLab Open
An effective and efficient application of Continuous Integration (CI) and Delivery (CD) requires software projects to follow certain principles and good practices. Configuring such a CI/CD pipeline is challenging and error-prone. Therefore…
View article: Configuration Smells in Continuous Delivery Pipelines: A Linter and A Six-Month Study on GitLab
Configuration Smells in Continuous Delivery Pipelines: A Linter and A Six-Month Study on GitLab Open
An effective and efficient application of Continuous Integration (CI) and Delivery (CD) requires software projects to follow certain principles and good practices. Configuring such a CI/CD pipeline is challenging and error-prone. Therefore…