Usability evaluation of a domain-specific language for defining aggregated processing tasks

The effective processing of Big Data sets often requires some programming knowledge from a prospective user’s part. This could prove costly to achieve, in terms of user training time and effort, depending on the level of previous experience. The premise, when dealing with large data sets, is that it should be as easy as possible for a user to prototype and test processing algorithms, in order to deal with them in an effective manner. For this reason, we have developed a domain- specific language meant to allow users to define data processing tasks as aggregates, consisting of atomic operations. Its goal is to do away with some of the complexities of traditional programming languages, by simplifying the representation model and providing a more intuitive process description tool for its users. This paper aims to evaluate the efficiency and effectiveness with which a novice user could employ our domain-specific language to define processing tasks, and then compare the results to those obtained while using the Python programming language. The experiments will be focused on task duration, description correctness and code interpretation, highlighting possible advantages and disadvantages observed during the usage of the two languages.