Automated Bug Reporting System with Keyword-Driven Framework

  • Palvika
  • Shatakshi
  • Yashika Sharma
  • Arvind DagurEmail author
  • Rahul Chaturvedi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 898)


In this paper, a keyword-driven framework approach has been investigated which is used for the automation testing. In this approach, we make a separate java file of each and every object, i.e., actions, test setup, and test scripts. It generates the report according to their status of execution (e.g., pass and fail). The report is an HTML format such as an excel sheet having columns, named as test cases name, keyword, description, execute, and result. In the proposed methodology, we have a keyword function library in which we define all the keywords belonging to the Web applications. Here, keywords are the different Web elements present in the Web application, and actions are performed on it. These actions are the functions which are a call from execution engine. After performing the entire test, it will write the status of the test cases in the report and then send it to the concern team. The implementation results show that the proposed approach has generated better results as compared to the existing approaches.


Bug report Test case Test script Test suits Execution engine Framework 


  1. 1.
    Jeong, G., Kim, S., Zimmermann, T.: Improving bug triage with bug tossing graphs. In: Proceedings of Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp. 111–120 (2009)Google Scholar
  2. 2.
    Yang, G., Zang, T., Lee, B.: Towards semi-automatic bug triage and severity prediction based on topic model and multi-feature of bug reports. In: 2013 IEEE 38th Annual Conference on Computer Software and Applications (COMPSAC), pp. 97–106. IEEE (2014)Google Scholar
  3. 3.
    Hui, J., Yuqing, L., Pei, L., Jing, G., Shuhang, G.: LKDT: a keyword-driven based distributed test framework. In: International Conference on Computer Science and Software Engineering, pp. 719–722 (2008)Google Scholar
  4. 4.
    Sun, C., Lo, D., Khoo, S.-C., Jiang, J.: Towards more accurate retrieval of duplicate bug reports. In: Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering (2011)Google Scholar
  5. 5.
    Panichella, A., Dit, B., Oliveto, R., Di Penta, M., Poshyvanyk, D., De Lucia, A.: How to effectively use topic models for software engineering tasks? An approach based on genetic algorithms. In: Proceedings of the 2013 International Conference on Software Engineering, pp. 522–531. IEEE Press (2013)Google Scholar
  6. 6.
    Bhattacharya, P., Neamtiu, I.: Fine-grained incremental learning and multi-feature tossing graphs to improve bug triaging. In: 2010 IEEE International Conference on Software Maintenance (ICSM), pp. 1–10. IEEE (2010)Google Scholar
  7. 7.
    Takala, T., Maunumaa, M., Katara, M.: An adapter framework for keyword-driven testing. In: Ninth International Conference on Quality Software, Department of Software Systems, Tampere University of technology, Finland, pp. 201–210 (2009)Google Scholar
  8. 8.
    Cubranic, D.: Automatic bug triage using text categorization. In: Seke 2004: Proceedings of the Sixteenth International Conference on Software Engineering & Knowledge Engineering (2004)Google Scholar
  9. 9.
    Nguyen, A., Nguyen, T., Al-Kofahi, J., Nguyen, H., Nguyen, T.: A topic-based approach for narrowing the search space of buggy files from a bug report. In: 2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 263–272 (2011)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Palvika
    • 1
  • Shatakshi
    • 1
  • Yashika Sharma
    • 1
  • Arvind Dagur
    • 1
    Email author
  • Rahul Chaturvedi
    • 1
  1. 1.Department of Computer EngineeringKrishna Engineering CollegeGhaziabadIndia

Personalised recommendations