Software projects produce large quantities of data such as feature requests, requirements, design artifacts, source code, tests, safety cases, release plans, and bug reports. If leveraged effectively, this data can be used to provide project intelligence that supports diverse software engineering activities such as release planning, impact analysis, and software analytics. However, project stakeholders often lack skills to formulate complex queries needed to retrieve, manipulate, and display the data in meaningful ways. To address these challenges we introduce TiQi, a natural language interface, which allows users to express software-related queries verbally or written in natural language. TiQi is a web-based tool. It visualizes available project data as a prompt to the user, accepts Natural Language (NL) queries, transforms those queries into SQL, and then executes the queries against a centralized or distributed database. Raw data is stored either directly in the database or retrieved dynamically at runtime from case tools and repositories such as Github and Jira. The transformed query is visualized back to the user as SQL and augmented UML, and raw data results are returned. Our tool demo can be found on YouTube at the following link:http://tinyurl.com/TIQIDemo.

Trace links provide critical support for numerous software engineering activities including safety analysis, compliance verification, test-case selection, and impact prediction. However, as the system evolves over time, there is a tendency for the quality of trace links to degrade into a tangle of inaccurate and untrusted links. This is especially true with the links between source-code and upstream artifacts such as requirements - because developers frequently refactor and change code without updating the links. We present TLE (Trace Link Evolver), a solution for automating the evolution of trace links as changes are introduced to source code. We use a set of heuristics, open source tools, and information retrieval methods to detect common change scenarios across different versions of software. Each change scenario is then associated with a set of link evolution heuristics which are used to evolve trace links. We evaluate our approach through a controlled experiment and also through applying it across 27 releases of the Cassandra Database System. Results show that the trace links evolved using our approach are significantly more accurate than those generated using information retrieval alone.