Enzo Cialini

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The architecture of a large software system is widely considered important for such reasons as: providing a common goal to the stakeholders in realising the envisaged system; helping to organise the various development teams; and capturing foundational design decisions early in the development. Studies have shown that defects originating in system(More)
Defect prediction models presented in the literature lack generalization unless the original study can be replicated using new datasets and in different organizational settings. Practitioners can also benefit from replicating studies in their own environment by gaining insights and comparing their findings with those reported. In this work, we replicated an(More)
<b>Context:</b> Number of defects fixed in a given month is used as an input for several project management decisions such as release time, maintenance effort estimation and software quality assessment. Past activity of developers and testers may help us understand the future number of reported defects. <b>Goal:</b> To find a simple and easy to implement(More)
Big data systems (BDSs) are complex, consisting of multiple interacting hardware and software components, such as distributed computing nodes, databases, and middleware. Any of these components can fail. Finding the failures' root causes is extremely laborious. Analysis of BDS-generated logs can speed up this process. The logs can also help improve testing(More)
Certain software defects require corrective changes repeatedly in a few components of the system. One type of such defects spans multiple components of the system, and we call such defects pervasive multiple-component defects (PMCDs). In this paper, we describe an empirical study of six releases of a large legacy software system (of approx. size 20 million(More)
Operational and usage profiles collected from customers provide developers and testers with valuable quantitative information on usage patterns of software being developed. Unfortunately, gathering such profiles from a large set of customers can be challenging due to time and resource constraints. In this paper we propose to use information about customer(More)
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