Konstantin Kuznetsov

Background. Mutation analysis is the premier technique for evaluating test suite quality estimating residual software defects. However, the reliability of mutation analysis is hampered by equivalent mutants which are undetectable by test cases. Reliably detecting and eliminating killable mutants is difficult as it is highly program and location dependent. Statistical estimation of killable mutants seems to be a promising approach to tackle this problem. Aims. Frequency-based species estimation methods have been proposed as a solution for several related problems in software testing. This paper investigates whether such frequency-based estimation methods can accurately estimate the number of killable mutants. Method. We conducted a large-scale empirical study on the ability of twelve widely known frequency-based estimators to predict the number of killable mutants in ten mature software projects. Result. Our investigation finds limited or no evidence that any of the statistical estimators are able to consistently predict the number of killable mutants in projects evaluated. Conclusion. We found that the investigated estimators lack sufficient predictive power and cannot produce reliable and useful estimates of killable mutants.

Paper and digital forms are widely used to collect user information across multiple domains, such as research, healthcare, and education. However, both types still lack application and follow-up interpretation: Paper forms need to be digitised meticulously to be analyzed or shared with team members efficiently; in comparison, digital forms cannot convey handwriting and might require technical literacy. We present the FormTwin data collection tool—an alternative to online forms, which allows for the efficient reuse of existent paper forms while providing the convenience of digital forms. FormTwin can digitise a wide range of forms with the integrated form annotation application. Then, it combines two input channels: A stylus on a tablet and a digital smart pen on plain paper, which duplicates the input on a mobile application in real-time. We aim to improve the efficiency and accessibility of data collection for practitioners with a modular system that combines both the digital accessibility of digital forms with keeping the needed technical literacy low and retaining the quality of hand-drawn sketches.

Contract cheating, i.e., when a student employs another person to participate in an exam, appears to become a growing problem in academia. Cases of paid test takers are repeatedly reported in the media, but the number of unreported cases is unclear. Proctoring systems as a countermeasure are typically not appreciated by students and teachers because they may violate the students’ privacy and can be imprecise and nontransparent. In this work, we propose to use automatic handwriting analysis based on digital ballpoint pens to identify individuals during exams unobtrusively. We implement a system that enables continuous authentication of the user during exams. We use a deep neural network architecture to model a user’s handwriting style. An evaluation based on the large Deepwriting dataset shows that our system can successfully differentiate between the handwriting styles of different authors and hence detect simulated cases of contract cheating. In addition, we conducted a small validation study using digital ballpoint pens to assess the system’s reliability in a more realistic environment.