Information obtained by merging data extracted from problem reporting systems -such as Bugzilla -and versioning systems -such as Concurrent Version System (CVS) -is widely used in quality assessment approaches.This paper attempts to shed some light on threats and difficulties faced when trying to integrate information extracted from Mozilla CVS and bug repositories. Indeed, the heterogeneity of Mozilla bug reports, often dealing with non-defect issues, and lacking of traceable information may undermine validity of quality assessment approaches relying on repositories integration.In the reported Mozilla case study, we observed that available integration heuristics are unable to recover thousands of traceability links. Furthermore, Bugzilla classification mechanisms do not enforce a distinction between different kinds of maintenance activities.Obtained evidence suggests that a large amount of information is lost; we conjecture that to benefit from CVS and problem reporting systems, more systematic issue classification and more reliable traceability mechanisms are needed.

Abstract. Probabilistic and learned approaches to student modeling are attractive because of the uncertainty surrounding the student skills assessment and because of the need to automatize the process. Item to item structures readily lend themselves to probabilistic and fully learned models because they are solely composed of observable nodes, like answers to test questions. Their structure is also well grounded in the cognitive theory of knowledge spaces. We study the effectiveness of two Bayesian frameworks to learn item to item structures and to use the induced structures to predict item outcome from a subset of evidence. One approach, POKS, relies on a naive Bayes framework whereas the other is based on the Bayesian network learning and inference framework. Both approaches are assessed over their predictive ability and their computational efficiency in different experimental simulations. The results from simulations over three data sets show that they both can effectively perform accurate predictions, but POKS generally displays higher predictive power than the Bayesian network. Moreover, the simplicity of POKS translates to a time efficiency of one to three orders of magnitude greater than the Bayesian network runs. We furhter explore the use of the item to item approach for handling concepts mastery assessment. The approach investigated consist in augmenting an initial set of observations, based on inferences with the item to item structure, and feed the augmented set to a Bayesian network containing a number of concepts. The results show that augmented set can effectively improve predictive power of a Bayesian network for item outcome, but that improvement does not transfer to the concept assessment in this particular experiment. We discuss different explanations for the results and outline future research avenues.

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