Mining biometric data to predict programmer expertise and task difficulty
作者： Seolhwa Lee , Danial Hooshyar , Hyesung Ji , Kichun Nam , Heuiseok Lim
DOI: 10.1007/S10586-017-0746-2
关键词:
摘要: Programming mistakes frequently waste software developers’ time and may lead to the introduction of bugs into their software, causing serious risks for customers. Using correlation between various process metrics defects, earlier work has traditionally attempted spot such bug risks. However, this study departs from previous works in examining a more direct method using psycho-physiological sensors data detect difficulty program comprehension tasks programmer level expertise. By conducting with 38 expert novice programmers, we investigated how well an electroencephalography eye-tracker can be utilized predicting expertise (novice/expert) task (easy/difficult). both sensors, could predict 64.9 97.7% precision 68.6 96.4% recall, respectively. The result shows it is possible perceived developers data. In addition, found that while single biometric sensor good results, composition best overall performance.
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