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Program queries can answer important software engineering questions that range from "which expressions are cast to this type?" over "does my program attempt to read from a closed file?" to "does my code follow the prescribed design?". In this paper, we present a comprehensive tool suite for querying Java programs. It consists of the logic program query language SOUL, the CAVA library of predicates for quantifying over an Eclipse workspace and the Eclipse plugin BARISTA for launching queries and inspecting their results. BARISTA allows other Eclipse plugins to peruse program query results which is facilitated by the symbiosis of SOUL with Java -setting SOUL apart from other program query languages. This symbiosis enables the CAVA library to forego the predominant transcription to logic facts of the queried program. Instead, the library queries the actual AST nodes used by Eclipse itself, making it trivial for any Eclipse plugin to find the AST nodes that correspond to a query result. Moreover, such plugins do not have to worry about having queried stale program information. We illustrate the extensibility of our suite by implementing a tool for co-evolving source code and annotations using program queries.

Abstract-System maintainers face several challenges stemming from a system and its library dependencies evolving separately. Novice maintainers may lack the historical knowledge required to efficiently manage an inherited system. While some libraries are regularly updated, some systems keep a dependency on older versions. On the other hand, maintainers may be unaware that other systems have settled on a different version of a library. In this paper, we visualize how the dependency relation between a system and its dependencies evolves from two perspectives. Our system-centric dependency plots (SDP) visualize the successive library versions a system depends on over time. The radial layout and heat-map metaphor provide visual clues about the change in dependencies along the system's release history. From this perspective, maintainers can navigate to a library-centric dependants diffusion plot (LDP). The LDP is a time-series visualization that shows the diffusion of users across the different versions of a library. We demonstrate on real-world systems how maintainers can benefit from our visualizations through four case scenarios.

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Test smells are, analogously to code smells, defined as the characteristics exhibited by poorly designed unit tests. Their negative impact on test effectiveness, understanding, and maintenance has been demonstrated by several empirical studies.However, the scope of these studies has been limited mostly to JAVA in combination with the JUNIT testing framework. Results for other language and framework combinations are -despite their prevalence in practice-few and far between, which might skew our understanding of test smells. The combination of SCALA and SCALATEST, for instance, offers more comprehensive means for defining and reusing test fixtures, thereby possibly reducing the diffusion and perception of fixture-related test smells.This paper therefore reports on two empirical studies conducted for this combination. In the first study, we analyse the tests of 164 open-source SCALA projects hosted on GITHUB for the diffusion of test smells. This required the transposition of their original definition to this new context, and the implementation of a tool (SOCRATES) for their automated detection. In the second study, we assess the perception by and the ability of 14 SCALA developers to identify test smells. For this context, our results show (i) that test smells have a low diffusion across test classes, (ii) that the most frequently occurring test smells are LAZY TEST, EAGER TEST, and ASSERTION ROULETTE, and (iii) that many developers were able to perceive but not to identify the smells.