Content management systems (CMSs) such as Joomla and WordPress dominate today’s web. Enabled by standardized extensions, administrators can build powerful web applications for diverse customer demands. However, developing CMS extensions requires sophisticated technical knowledge, and the complex code structure of an extension gives rise to errors during typical development and migration scenarios. Model-driven development (MDD) seems to be a promising paradigm to address these challenges; however, it has not found adoption in the CMS domain yet. Systematic evidence of the benefit of applying MDD in this domain could facilitate its adoption; however, an empirical investigation of this benefit is currently lacking. In this paper, we present a mixed-method empirical investigation of applying MDD in the CMS domain, based on an interview suite, a controlled experiment, a field experiment, and case studies. During the experiments, we used JooMDD, an MDD infrastructure instantiation for CMS extensions. This infrastructure, which is also presented in this work, consists of a DSL with model editors, code generators, and reverse engineering facilities. We consider three scenarios of developing new (both independent and dependent) CMS extensions and of migrating existing ones to a new major platform version. The experienced developers in our interviews acknowledge the relevance of these scenarios and report on experiences that render them suitable candidates for a successful application of MDD. We found a particularly high relevance of the migration scenario. Our experiments largely confirm the potentials and limits of MDD as identified for other domains. In particular, we found a productivity increase up to factor 11.7 and a quality increase up to factor 2.4 during the development of CMS extensions. Furthermore, our observations highlight the importance of good tooling that seamlessly integrates with already used tool environments and processes.
Domain-Specific Languages (DSLs) found application in different domains. The development of Model-Driven Development (MDD) components is facilitated by a wealth of frameworks like EMF, Xtext, and Xtend. However, the development of the necessary IDE components still can take up to several weeks or even months until it can be used in a production environment. The first step during the development of such an MDD infrastructure is to analyse a set of reference applications to deduce the DSL used by the domain experts and the templates used in the generator. The analysis requires technical expertise and is usually performed by MDD infrastructure developers, who have to adhere to a close communication with domain experts and are exposed to high cognitive load and time-consuming tasks. The objective of this PhD project is to reduce the initial effort during the creation of new MDD infrastructure facilities for either a new domain or newly discovered platforms within a known domain. This should be made possible by the (semi-)automatic analysis of multiple codebases using Code Clone Detection (CCD) tools in a defined process flow. Code clones represent schematically redundant and generic code fragments which were found in the provided codebase. In the process, the key steps include (i) choosing appropriate reference applications (ii) distinguishing the codebase by clustering the files, (iii) reviewing the quality of the clusters, (iv) analysing the cluster by tailored CCD, and (v) transforming of the code clones, depending on the code clone type, to extract a DSL and the corresponding generator templates. CCS CONCEPTS • Information systems → Clustering; • Software and its engineering → Domain specific languages; Model-driven software engineering.
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