Milan Nosáľ

Milan Nosáľ, Jaroslav Porubän, Matúš Sulír

Pure embedding as an implementation strategy of domain-specific languages (DSLs) benefits from low implementation costs. On the other hand, it introduces undesired syntactic noise that impedes involvement of non-programming domain experts. Due to this, pure embedded DSLs are generally not intended for, nor used by, non-programmers. In this work, we try to challenge this state by experimenting with inexpensive customizations of the host IDE (Integrated Development Environment) to reduce the negative impact of syntactic noise. We present several techniques and recommendations based on standard IDE features (e.g., file templates, code folding, etc.) that aim to reduce syntactic noise and generally improve the user experience with pure embedded DSLs. The techniques are presented using a NetBeans IDE case study. The goal of the proposed techniques is to improve the user experience with pure embedded DSLs with a focus on the involvement of non-programming domain experts (or non-programmers in general). The proposed techniques were evaluated using a controlled experiment. The experiment compared a group using Ruby and non-modified RubyMine IDE versus a group using Java and NetBeans IDE customized to use the proposed techniques. Experiment results indicate that even inexpensive host IDE customizations can significantly alleviate issues caused by the syntactic noise: Java with its inflexible syntax performed better than Ruby with its concise syntax.

Matúš Sulír, Milan Nosáľ, Jaroslav Porubän

A concern can be characterized as a developer's intent behind a piece of code, often not explicitly captured in it. We discuss a technique of recording concerns using source code annotations (concern annotations). Using two studies and two controlled experiments, we seek to answer the following 3 research questions: 1) Do programmers' mental models overlap? 2) How do developers use shared concern annotations when they are available? 3) Does using annotations created by others improve program comprehension and maintenance correctness, time and confidence? The first study shows that developers' mental models, recorded using concern annotations, overlap and thus can be shared. The second study shows that shared concern annotations can be used during program comprehension for the following purposes: hypotheses confirmation, feature location, obtaining new knowledge, finding relationships and maintenance notes. The first controlled experiment with students showed that the presence of annotations significantly reduced program comprehension and maintenance time by 34%. The second controlled experiment was a differentiated replication of the first one, focused on industrial developers. It showed a 33% significant improvement in correctness. We conclude that concern annotations are a viable way to share developers' thoughts.