NSF SHF: Medium: Collaborative Research: Semantically-Enhanced Software Traceability for Supporting Human-Centric Tasks

Project Description (NSF CCF-1901059)

PI: Jane Cleland-Huang; co-PIs: Meng Jiang and Ronald A. Metoyer

Achieving accurate, complete, trustworthy and usable traceability across software-intensive systems can be extremely beneficial as the underlying network of traceability links can be used to answer diverse questions about the software system and its development process. However, in practice, many software projects suffer from inadequate or inaccurate traceability due to the cost and difficulty of manually creating and maintaining trace links. This research will develop a holistic, interactive tracing environment, which incorporates diverse algorithmic solutions for dynamically generating trace links, visualizing the results, and guiding the user through the interactive process of using the results to support diverse Software Engineering tasks. Automating the creation and maintenance of accurate traceability links offers significant potential for industrial impact. For example, traceability is required by certifying bodies in safety-critical domains and can help in the construction and delivery of high quality, competitive, timely products. The cross-disciplinary nature of the team will introduce new opportunities for software engineers, data scientists, and human-computer interaction experts to collaborate in addressing open Software Engineering challenges and will provide research opportunities for diverse and underrepresented students.

The research will explore challenging problems at the intersection of software engineering, semantic text mining, and visualization. It will directly address one of the prominent causes of trace-link inaccuracy caused by the inability of current algorithms to reason over deep semantics of underlying software artifacts such as requirements, design, and code. First, the researchers will investigate semantically enhanced traceability algorithms that generate trace links, even in the absence of shared textual representations. Second, the work will develop a holistic tracing solution that dynamically configures a trace engine to leverage diverse tracing techniques such as semantic traceability, trace link evolution, and other existing techniques. Finally, given a diverse set of trace links with different degrees of accuracy, the research team will design, develop, and publicly release a novel, interactive, visual interface that enables users to understand the provenance and trustworthiness of each link while providing a clear rationale for trace query results.


Email: mjiang2@nd.edu

Graduate Student

Qingkai Zeng: PhD student (2018-)
Email: qzeng@nd.edu
Text mining: Deep models for scientific information extraction
Wenhao Yu: PhD student (2019-)
Email: wyu1 [at] nd.edu
Representation learning: Incorporating behavioral intention into text mining
Tong Zhao: Master student (2017-2018), PhD student (2018-)
Email: tzhao2 [at] nd.edu
Suspicious behavior detection: Graph anomaly detection and graph neural networks