The frequency of different kinds of error made by students learning to write computer programs has long been of interest to researchers and educators. In the past, various studies investigated this topic, usually by recording and analysing compiler error messages, and producing tables of relative frequencies of specific errors diagnostics produced by the compiler. In this paper, we improve on such prior studies by investigating actual logical errors in student code, as opposed to diagnostic messages produced by the compiler. The actual errors reported here are more precise, more detailed and more accurate than the diagnostic produced automatically. In order to present frequencies of actual errors, error categories were developed and validated, and student code captured at time of compilation failure was manually analysed by multiple researchers. The results show that error causes can be manually analysed by independent researchers with good reliability. The resulting table of error frequencies shows that prior work using diagnostic messages tended to group some distinct errors together in single categories, which can now be listed more accurately.
McCall et al. (Wed,) studied this question.