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Detecting values-violating defects in source code (mobile apps)

Primary supervisor

Humphrey Obie

Co-supervisors


This project will analyse a large corpus of mobile apps software artefacts including source code, and then use machine learning/rule-based techniques to develop an "app feature values miner" that analyses this corpus to identify potential human values in the app, potential app features that relate to these values, and relationships between features at different levels of granularity and the end-user human values.

Student cohort

Single Semester

URLs/references

  • D. Mougouei, "Engineering human values in software through value programming," ser. ICSEW’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 133–136.
  • M. Naseri, N. P. Borges, A. Zeller, and R. Rouvoy, "Accessileaks: Investigating privacy leaks exposed by the android accessibility service," Proceedings on Privacy Enhancing Technologies, vol. 2019, no. 2, pp. 291 – 305, 01 Apr. 2019.
  • H. Obie, W. Hussain, X. Xia, J. Grundy, L. Li, B. Turhan, J. Whittle and M. Shahin, "A First Look at Human Values-Violation in Mobile Apps," ICSE ’21: IEEE/ACM International Conference on Software Engineering, Madrid, Spain.

Required knowledge

  • Programming experience (preferably Python/Java)
  • Mobile app development
  • Machine learning