Related papers: Retrospective: Data Mining Static Code Attributes …

Many analysis and prediction tasks require the extraction of structured data from unstructured texts. However, an annotation scheme and a training dataset have not been available for training machine learning models to mine structured data…

Organizational, political, and configuration mistakes in the deployment of a static source code analysis tool within a software development organization can result in most of the value of the tool being lost, even while apparently meeting…

Accurately predicting faulty software units helps practitioners target faulty units and prioritize their efforts to maintain software quality. Prior studies use machine-learning models to detect faulty software code. We revisit past studies…

The community of program optimisation and analysis, code performance evaluation, parallelisation and optimising compilation has published since many decades hundreds of research and engineering articles in major conferences and journals.…

Developers often opt for easier but non-optimal implementation to meet deadlines or create rapid prototypes, leading to additional effort known as technical debt to improve the code later. Oftentimes, developers explicitly document the…

Existing work on the practical impact of software engineering (SE) research examines industrial relevance rather than adoption of study results, hence the question of how results have been practically applied remains open. To answer this…

Source Code Summarization is the task of writing short, natural language descriptions of source code. The main use for these descriptions is in software documentation e.g. the one-sentence Java method descriptions in JavaDocs. Code…

Feature engineering has become one of the most important steps to improve model prediction performance, and to produce quality datasets. However, this process requires non-trivial domain-knowledge which involves a time-consuming process.…

Context: Software systems are in continuous evolution through source code changes to fixing bugs, adding new functionalities and improving the internal architecture. All these practices are recorded in the version history, which can be…

Deep learning is being used extensively in a variety of software engineering tasks, e.g., program classification and defect prediction. Although the technique eliminates the required process of feature engineering, the construction of…

Context: Software metrics, as one form of static analyses, is a commonly used approach in software engineering in order to understand the state of a software system, in particular to identify potential areas prone to defects. Family-based…

Recent advancements in graph neural networks (GNNs) for link prediction have introduced sophisticated training techniques and model architectures. However, reliance on outdated baselines may exaggerate the benefits of these new approaches.…

Supervised machine learning and predictive models have achieved an impressive standard today, enabling us to answer questions that were inconceivable a few years ago. Besides these successes, it becomes clear, that beyond pure prediction,…

Technical debt is a metaphor indicating sub-optimal solutions implemented for short-term benefits by sacrificing the long-term maintainability and evolvability of software. A special type of technical debt is explicitly admitted by software…

There has been an increase of interest in code search using natural language. Assessing the performance of such code search models can be difficult without a readily available evaluation suite. In this paper, we present an evaluation…

This paper proposes an approach for a tool-agnostic and heterogeneous static code analysis toolchain in combination with an exchange format. This approach enhances both traceability and comparability of analysis results. State of the art…

Static software checking tools are useful as an additional automated software inspection step that can easily be integrated in the development cycle and assist in creating secure, reliable and high quality code. However, an often quoted…

To effectively guide the exploration of the code transform space for automated code evolution techniques, we present in this paper the first approach for structurally predicting code transforms at the level of AST nodes using conditional…

Notebooks provide an interactive environment for programmers to develop code, analyse data and inject interleaved visualizations in a single environment. Despite their flexibility, a major pitfall that data scientists encounter is…

The goal of this thesis is to advance the exploration of the statistical language learning design space. In pursuit of that goal, the thesis makes two main theoretical contributions: (i) it identifies a new class of designs by specifying an…