Workshop

Voxxed Days Ticino 2026

Software development involves iterations of writing, running, testing, and debugging code. When fixing a defect, developers construct a mental model of the system that explains the defect and eventually identifies its cause. However, filtering complete, coherent, and reliable information from a running system is not an easy task: Using a simple approach, like generic logging, is often ineffective because it deconstructs and flattens the state into textual data, thus requiring ad-hoc understanding and processing. On the other hand, collecting structured information in form of objects to observe and understand a precise property of the system requires specialized ad-hoc code, decoupled from the system's domain, and usually not reusable. We present ShoreLine, a domain-specific data collection framework that enables the developers to extract selected information about a running system. The developer is able to take a snapshot of all the information deemed relevant about a piece of code by writing few lines of code, thus enabling structured and effective logging and reporting of errors. We detail our framework in the context of a bug reporting platform, and illustrate how such an approach can be used to create in-depth and reliable domain-specific bug reports.

Code annotations are the core of the main APIs and frameworks for enterprise development, and are widely used on several applications. However, despite these APIs and frameworks made advanced uses of annotations, the language API for annotation reading is far from their needs. In particular, annotation reading is still a relatively complex task, that can consume a lot of development time and that can couple the framework internal structure to its annotations. This paper proposes an annotation-based API to retrieve metadata from code annotations and populate an instance with meta-information ready to be used by the framework. The proposed API is based on best practices and approaches for metadata definition documented on patterns, and has been implemented by a framework named Esfinge Metadata. We evaluated the approach by refactoring an existing framework to use it through Esfinge Metadata. The original and the refactored versions are compared using several code assessment techniques, such as software metrics, and bad smells detection, followed by a qualitative analysis based on source code inspection. As a result, the case study revealed that the usage of the proposed API can reduce the coupling between the metadata reading code and the annotations.

Stack Overflow has become a fundamental resource for developers, becoming the de facto Question and Answer (Q&A) website, and one of the standard unstructured data sources for software engineering research to mine knowledge about development. We present SODA, the Stack Overflow Dataset Almanac, a tool that helps researchers and developers to better understand the trends of discussion topics in Stack Overflow, based on the available tagging system. SODA provides an effective visualization to support the analysis of topics in different time intervals and frames, leveraging single or co-occurrent tags. We show, through simple usage scenarios, how SODA can be used to find interesting peculiar moments in the evolution of Stack Overflow discussions that closely match specific recent events in the area of software development. SODA is available at http://rio.inf.usi.ch/soda/

We focus on the problem of searching components based on semantic queries on their provided interface. Although semantics-based search has long been advocated as a key enabler in the context of component-based software development and, more recently, service-oriented computing, no practical and scalable approach has been proposed yet. This paper presents a promising model-based search technique for interface behaviors based on operational specifications, called behavioral equivalence models (Bems). Semantic queries are expressed equationally, following an algebraic specification style. The search engine tries to match specifications against queries. This can be done quite efficiently by encoding Bems into relational models and queries into relational logic formulae, whose satisfiability is checked with the SAT-based constraint solver KodKod. We can report on an initial very promising assessment of the proposed technique, which has been applied to searching components in Java libraries providing container functionalities.

This paper proposes an approach for the specification of the behavior of software components that implement data abstractions. By generalizing the approach of behavior models using graph transformation, we provide a concise specification for data abstractions that describes the relationship between the internal state, represented in a canonical form, and the observers of the component. Graph transformation also supports the generation of behavior models that are amenable to verification. To this end, we provide a translation approach into an LTL model on which we can express useful properties that can be model-checked with a SAT solver.