Johannes Haertel

Universität Koblenz-Landau · Department of Computer Science

Conventional wisdom on Model-Driven Engineering (MDE) suggests that this software discipline is the key to achieve superior automation, whether it be refactoring, simulation, or code generation. However, the diversity of employed languages and technologies blurs the picture making it difficult to analyze existing MDE-based projects in order to retr...

Graph data models are interesting in various domains, in part because of the intuitiveness and flexibility they offer compared to relational models. Specialized query languages, such as Cypher for property graphs or SPARQL for RDF, facilitate their use. In this paper, we present an empirical study on the usage of graph-based query languages in open...

APIs can be classified according to the programming domains (e.g., GUIs, databases, collections, or security) that they address. Such classification is vital in searching repositories (e.g., the Maven Central Repository for Java) and for understanding the technology stack used in software projects. We apply hierarchical clustering to a curated suit...

Selecting and properly using approaches for DSL implementation can be challenging, given their variety and complexity. To support developers, we present the software chrestomathy MetaLib, a well-organized and well-documented collection of DSL implementations useful for learning. We focus on basic metaprogramming techniques for implementing DSL synt...

The context of the reported research is the documentation of software technologies such as object/relational mappers, web-application frameworks, or code generators. We assume that documentation should model a macroscopic view on usage scenarios of technologies in terms of involved artifacts, leveraged software languages, data flows, conformance re...

We present a new feedback strategy implemented in a natural language generation-based e-learning system for German as a second language (L2). Although the system recognizes a large proportion of the grammar errors in learner-produced written sentences, its automatically generated feedback only addresses errors against rules that are relevant at the...

We describe a method and a corresponding tool for grammar-based test-data generation (GBTG). The basic generation principle is to enumerate test data based on grammatical choices. However, generation is broken down into two phases to deal with context-sensitive properties in an efficient and convenient manner. The first phase enumerates test data (...