Dependency cycles are commonly recognized as one of the most critical quality anti-patterns. Cycles compromise the modularity of a system, prevent proper reuse and increase the cost of maintenance and testing. Many tools are capable of detecting and visualizing package cycles existing within software projects. Unfortunately, detecting cycles is only half of the work. Once detected, cycles need to be removed and this typically results in a complex process that is only partially supported by current tools. We propose a tool that offers an intelligent guidance mechanism to support developers in removing package cycles. Our tool, Marea, simulates different refactoring strategies and suggests the most cost-effective sequence of refactoring operations that will break the cycle. The optimal refactoring strategy is determined based on a custom profit function. Our approach has been validated on multiple projects and executes in linear time.

Architectural decisions are often encoded in the form of constraints and guidelines. Non-functional requirements can be ensured by checking the conformance of the implementation against this kind of invariant. Conformance checking is often a costly and error-prone process that involves the use of multiple tools, differing in effectiveness, complexity and scope of applicability. To reduce the overall effort entailed by this activity, we propose a novel approach that supports verification of human-readable declarative rules through the use of adapted off-the-shelf tools. Our approach consists of a rule specification DSL, called Dicto, and a tool coordination framework, called Probo. The approach has been implemented in a soon to be evaluated prototype.

Subtype polymorphism is a cornerstone of object-oriented programming. By hiding variability in behavior behind a uniform interface, polymorphism decouples clients from providers and thus enables genericity, modularity and extensibility. At the same time, however, it scatters the implementation of the behavior over multiple classes thus potentially hampering program comprehension. The extent to which polymorphism is used in real programs and the impact of polymorphism on program comprehension are not very well understood. We report on a preliminary study of the prevalence of polymorphism in several hundred open source software systems written in Smalltalk, one of the oldest object-oriented programming languages, and in Java, one of the most widespread ones. Although a large portion of the call sites in these systems are polymorphic, a majority have a small number of potential candidates. Smalltalk uses polymorphism to a much greater extent than Java. We discuss how these findings can be used as input for more detailed studies in program comprehension and for better developer support in the IDE.

Software erosion can be controlled by periodically checking for consistency between the de facto architecture and its theoretical counterpart. Studies show that this process is often not automated and that developers still rely heavily on manual reviews, despite the availability of a large number of tools. This is partially due to the high cost involved in setting up and maintaining tool-specific and incompatible test specifications that replicate otherwise documented invariants. To reduce this cost, our approach consists in unifying the functionality provided by existing tools under the umbrella of a common business-readable DSL. By using a declarative language, we are able to write tool-agnostic rules that are simple enough to be understood by untrained stakeholders and, at the same time, can be interpreted as a rigorous specification for checking architecture conformance.

Software corpora facilitate reproducibility of analyses, however, static analysis for an entire corpus still requires considerable effort, often duplicated unnecessarily by multiple users. Moreover, most corpora are designed for single languages increasing the effort for cross-language analysis. To address these aspects we propose Pangea, an infrastructure allowing fast development of static analyses on multi-language corpora. Pangea uses language-independent meta-models stored as object model snapshots that can be directly loaded into memory and queryed without any parsing overhead. To reduce the effort of performing static analyses, Pangea provides out-of-the box support for: creating and refining analyses in a dedicated environment, deploying an analysis on an entire corpus, using a runner that supports parallel execution, and exporting results in various formats. In this tool demonstration we introduce Pangea and provide several usage scenarios that illustrate how it reduces the cost of analysis.