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From automated customer support to virtual assistants, conversational agents have transformed everyday interactions, yet despite phenomenal progress, no agent exists for programming tasks. To understand the design space of such an agent, we prototyped PairBuddy—an interactive pair programming partner—based on research from conversational agents, software engineering, education, human-robot interactions, psychology, and artificial intelligence. We iterated PairBuddy’s design using a series of Wizard-of-Oz studies. Our pilot study of six programmers showed promising results and provided insights toward PairBuddy’s interface design. Our second study of 14 programmers was positively praised across all skill levels. PairBuddy’s active application of soft skills—adaptability, motivation, and social presence—as a navigator increased participants’ confidence and trust, while its technical skills—code contributions, just-in-time feedback, and creativity support—as a driver helped participants realize their own solutions. PairBuddy takes the first step towards an Alexa-like programming partner. 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.