Abstract
Modern-day computers and processors are too complicated for a human to understand. Software engineers frequently reuse and patch existing code rather than starting from scratch, but this eventually leads to the accretion of multigenerational code of great opacity whose behaviour no one can fully predict. One troubleshooting technique that software engineers often apply is machine learning: in essence, using machines to diagnose the faults in other machines. A machine learning program will test the response of a new piece of software to many different combinations of inputs and look for patterns in the results that suggest how the software may be flawed. It is really doing the same thing a human would do – learning from the past to predict the future – but a computer can try many more combinations and can detect more deeply hidden patterns than a human can.
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The confusion matrix is the 2 ⋅ 2 matrix of true positives, true negatives, false positives, and false negatives. As Shepperd pointed out, focusing on only part of this matrix can lead to very misleading results. If 95 percent of the software being tested works correctly, then a fault detection algorithm that certifies everything as correct will give true results 95 percent of the time. Nevertheless, it will be absolutely useless!
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© 2013 Springer International Publishing Switzerland
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Tveito, A., Bruaset, A.M. (2013). Harmonizing the Babel of Voices. In: Bruaset, A., Tveito, A. (eds) Conversations About Challenges in Computing. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00209-5_11
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DOI: https://doi.org/10.1007/978-3-319-00209-5_11
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