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[Abridged] Context. Stellar activity is an important source of systematicerrors and uncertainties in the characterization of exoplanets. Most of thetechniques used to correct for this activity focus on an ad hoc data reduction.Aims. We have developed a software for the combined fit of transits and stellaractivity features in high-precision long-duration photometry. Our aim is totake advantage of the modelling to derive correct stellar and planetaryparameters, even in the case of strong stellar activity. Methods. We use ananalytic approach to model the light curve. The code KSint, modified by addingthe evolution of active regions, is implemented into our Bayesian modellingpackage PASTIS. The code is then applied to the light curve of CoRoT-2. Thelight curve is divided in segments to reduce the number of free parametersneeded by the fit. We perform a Markov chain Monte Carlo analysis in two ways.In the first, we perform a global and independent modelling of each segment ofthe light curve, transits are not normalized and are fitted together with theactivity features, and occulted features are taken into account during thetransit fit. In the second, we normalize the transits with a model of thenon-occulted activity features, and then we apply a standard transit fit, whichdoes not take the occulted features into account. Results. Our model recoversthe activity features coverage of the stellar surface and different rotationperiods for different features. We find variations in the transit parameters ofdifferent segments and show that they are likely due to the division applied tothe light curve. Neglecting stellar activity or even only bright spots whilenormalizing the transits yields a $\sim 1.2\sigma$ larger and $2.3\sigma$smaller transit depth, respectively. The stellar density also presents up to$2.5\sigma$ differences depending on the normalization technique...

Disentangling planetary and stellar activity features in the CoRoT-2 light curve