One of the surprising observations of traceability in practice is the under-utilization of existing trace links. Organizations often create links in order to meet compliance requirements, but then fail to capitalize on the potential benefits of those links to provide support for activities such as impact analysis, test regression selection, and coverage analysis. One of the major adoption barriers is caused by the lack of accessibility to the underlying trace data and the lack of skills many project stakeholders have for formulating complex trace queries. To address these challenges we introduce TiQi, a natural language approach, which allows users to write or speak trace queries in their own words. TiQi includes a vocabulary and associated grammar learned from analyzing NL queries collected from trace practitioners. It is evaluated against trace queries gathered from trace practitioners for two different project environments.

Software traceability is a sought-after, yet often elusive quality in large software-intensive systems primarily because the cost and effort of tracing can be overwhelming. State-of-the art solutions address this problem through utilizing trace retrieval techniques to automate the process of creating and maintaining trace links. However, there is no simple one- size-fits all solution to trace retrieval. As this paper will show, finding the right combination of tracing techniques can lead to significant improvements in the quality of generated links. We present a novel approach to trace retrieval in which the underlying infrastructure is configured at runtime to optimize trace quality. We utilize a machine-learning approach to search for the best configuration given an initial training set of validated trace links, a set of available tracing techniques specified in a feature model, and an architecture capable of instantiating all valid configurations of features. We evaluate our approach through a series of experiments using project data from the transportation, healthcare, and space exploration domains, and discuss its implementation in an industrial environment. Finally, we show how our approach can create a robust baseline against which new tracing techniques can be evaluated.

In many software intensive systems traceability is used to support a variety of software engineering activities such as impact analysis, compliance verification, and requirements validation. However, in practice, traceability links are often created towards the end of the project specifically for approval or certification purposes. This practice can result in inaccurate and incomplete traces, and also means that traceability links are not available to support early development efforts. We address these problems by presenting a trace recommender system which pushes recommendations to project stakeholders as they create or modify traceable artifacts. We also introduce the novel concept of a trace obligation, which is used to track satisfaction relations between a target artifact and a set of source artifacts. We model traceability events and subsequent actions, including user recommendations, using the Business Process Modeling Notation (BPMN). We demonstrate and evaluate the efficacy of our approach through an illustrative example and a simulation conducted using the software engineering artifacts of a robotic system for supporting arm rehabilitation. Our results show that tracking trace obligations and generating trace recommendations throughout the active phases of a project can lead to early construction of traceability knowledge.