Asked and Aswered - Intelligent Data Science in Software Projects (AA) is a joint project of the Software Engineering for Safety-Critical Systems Group and the Databases and Information Systems Group at Technische Universität Ilmenau. The project is funded by the German Research Foundation (DFG).

Software and systems engineering projects accumulate a mass of data in the form of domain documents, requirements, safety analysis, design, code, test cases, simulations, version control data, fault logs, model checkers, project plans and so on. When combined with the power of software analytics, this data can deliver the precise answers to questions that stakeholders demand. In particular, it can support decision making, process improvement, safety analysis, and a myriad of other software engineering tasks. AA will allow project stakeholders to pose a wide range of queries relevant to their common tasks. For example, a project manager might enquire about the status of the next release and the response would be an aggregated overview of planned features, their implementation state, and their latest test results if implemented. Similarly, a requirements engineer might request a coverage analysis at the source code level of all regulatory codes related to the onboard motor. In this case, the relevant regulatory codes could be highlighted inside the DOORS requirements management tool and annotated with source code coverage scores. In both cases, satisfying the information need requires transforming the abstract query into formal queries, retrieving raw data from a knowledge base, executing known software analytic functions, collating the results, and presenting them to the questioner.

Asked & Answered: Intelligent Data Analysis in Software Projects

The projects addresses several challenges:
  • Creation of a knowledge base satisfying the information needs for intelligent queries
  • Identification of question patterns and their transformation into formal queries
  • Identification of specific metrics of questions on software projects and efficient pre-processing of information regarding question answering
  • Development of an intuitive user frontend for presenting modular question generation methods
Funding Notice

This project is funded by the DFG under grant no: SA782/26.

  • Nadine Steinmetz, Kai-Uwe Sattler:
    What is in the KGQA benchmark datasets? Survey on Challenges in Datasets for Question Answering on Knowledge Graphs
    Journal on Data Semantics, Springer, 2021, To Appear
  • Nadine Steinmetz, Bhavya Senthil-Kumar, Kai-Uwe Sattler
    Conversational Question Answering Using a Shift of Context
    Proceedings of the Workshops of the EDBT/ICDT 2021 Joint Conference, March, 2021, Publisher CEUR Workshop Proceedings, Nicosia, Cyprus
  • Nadine Steinmetz, Samar Shahabi-Ghahfarokhi, Kai-Uwe Sattler
    Question Answering on OLAP-like Data Sources
    Proceedings of the Workshops of the EDBT/ICDT 2020 Joint Conference, March, To Appear, CEUR Workshop Proceedings, 2020, Publisher, Copenhagen, Denmark
  • Nadine Steinmetz, Kai-Uwe Sattler
    COALA -- A Rule-Based Approach to Answer Type Prediction
    Proceedings of the SeMantic AnsweR Type prediction task (SMART) at ISWC 2020 Semantic Web Challenge co-located with the 19th International Semantic Web Conference (ISWC 2020), CEUR-WS, Pages 29--40, SMART 2020, 2020, 1613-0073, Virtual Conference
  • Nadine Steinmetz, Ann-Katrin Arning und Kai-Uwe Sattler
    When is Harry Potters birthday? – Question Answering on Linked Data
    18. Fachtagung für Datenbanksysteme für Business, Technologie und Web (BTW), Demo Track, March, 2019
  • Nadine Steinmetz, Ann-Katrin Arning, Kai-Uwe Sattler
    From Natural Language Questions to SPARQL Queries: A Pattern-based Approach
    Datenbanksysteme für Business, Technologie und Web (BTW 2019), 18. Fachtagung des GI-Fachbereichs Datenbanken und Informationssysteme (DBIS), March, LNI, 2019, Publisher GI, Rostock, Germany