A Functional Classification of Text Annotations for Engineering Design

Describing and supplementing geometric shapes (parts) and layouts (assemblies) with relevant information is key for successful product design communication. 3D annotation tools are widely available in commercial systems, but they are generally used in the same manner as 2D annotations in traditional engineering drawings. The gap between technology and practices is particularly evident in plain text annotations. In this paper, we introduce a functional classification of text annotations to provide an information framework for shifting traditional annotation practices towards the Model-Based Definition (MBD) paradigm. In our view, the current classification of dimensions, tolerances, symbols, notes, and text does not stress the inherent properties of two broader categories: symbols and text. Symbol-based annotations use a symbolic language (mostly standardized) such as Geometric Dimensioning and Tolerancing (GD&T) to provide precise information about the implications of geometric imperfections in manufacturing, whereas notes and text are based on non-standardized and unstructured plain text, and can be used to convey design information. We advocate that text annotations can be characterized in four different functional types (objectives, requirements, rationale, and intent), which should be classified as such when annotations are added to a model. The identification and definition of a formalized structure and syntax can enable the management of the annotations as separate entities, thus leveraging their individual features, or as a group to gain a global and collective view of the design problem. The proposed classification was tested with a group of users in a redesign task that involved a series of geometric changes to an annotated assembly model

Recovering from a Decade: A Systematic Mapping of Information Retrieval Approaches to Software Traceability

Engineers in large-scale software development have to manage large amounts of information, spread across many artifacts. Several researchers have proposed expressing retrieval of trace links among artifacts, i.e. trace recovery, as an Information Retrieval (IR) problem. The objective of this study is to produce a map of work on IR-based trace recovery, with a particular focus on previous evaluations and strength of evidence. We conducted a systematic mapping of IR-based trace recovery. Of the 79 publications classified, a majority applied algebraic IR models. While a set of studies on students indicate that IR-based trace recovery tools support certain work tasks, most previous studies do not go beyond reporting precision and recall of candidate trace links from evaluations using datasets containing less than 500 artifacts. Our review identified a need of industrial case studies. Furthermore, we conclude that the overall quality of reporting should be improved regarding both context and tool details, measures reported, and use of IR terminology. Finally, based on our empirical findings, we present suggestions on how to advance research on IR-based trace recovery