A non-invasive method for the conformance assessment of pair programming practices based on hierarchical hidden Markov models

Abstract

We specify a non-invasive method allowing to estimate the time each developer of a pair spends over the development activity, during Pair Programming. The method works by performing first a behavioural fingerprinting of each developer - based on low level event logs - which then is used to operate a segmentation over the log sequence produced by the pair: in a timelined log event sequence this is equivalent to estimating the times of the switching between developers. We model the individual developer's behaviour by means of a Markov Chain - inferred from the logs - and model the developers' role-switching process by a further, higher level, Markov Chain. The overall model consisting in the two nested Markov Chains belongs to the class of Hierarchical Hidden Markov Models. The method could be used not only to assess the degree of conformance with respect to predefined Pair Programming switch-times policies, but also to capture the characteristics of a given programmers pair's switching process, namely in the context of Pair Programming effectiveness studies. © Springer-Verlag Berlin Heidelberg 2007.