Bad construction of modeled care pathways can lead to satisfiability problems during the pathway execution. These problems can ultimately result in medical errors and need to be checked as formally as possible. Therefore, this study proposes a set of algorithms using a free open-source library dedicated to constraint programming allied with a DSL to encode and verify care pathways, checking four possible problems: states in deadlock, non-determinism, inaccessible steps and transitions with logically equivalent guard conditions. We then test our algorithms in 84 real care pathways used both in hospitals and surgeries. Using our algorithms, we were able to find 200 problems taking less than 1 second to complete the verification on most pathways.

Software unbundling consists of dividing an existing software artifact into smaller ones. Unbundling can be useful for removing clutter from the original application or separating different features that may not share the same purpose, or simply for isolating an emergent functionality that merits to be an application on its own. This phenomenon is frequent with mobile apps and it is also propagating to APIs. This paper proposes a first empirical study on unbundling to understand its effects on popular APIs. We explore the possibilities of splitting libraries into 2 or more bundles based on the use that their client projects make of them. We mine over than 71,000 client projects of 10 open source APIs and automatically generate 2,090 sub-APIs to then study their properties. We find that it is possible to have sets of different ways of using a given API and to unbundle it accordingly; the bundles can vary their representativeness and uniqueness, which is analyzed thoroughly in this study.

Abstract Clinical guidelines specify sequences of steps (care pathways) to treat patients with single conditions. Increasingly, many patients exhibit “multimorbidity,” several chronic conditions needing concurrent treatment. However, applying multiple guidelines in parallel can lead to conflicts, eg, between prescribed drugs, lifestyle intervention recommendations, or treatment schedules. In computer science, process languages used to design and reason about software development and business process management are similar to clinical pathways. Using formal model transformation, composition and analysis methods, models can be combined and conflicts detected and resolved. We propose BPMN+V, a data‐driven formal model for clinical care pathways, as an extension of Business Process Model and Notation. We describe a method for conflict detection using a transformation of BPMN+V to Coloured Petri Nets and a state‐space method for detection of conflict in composed models. We present results from a case study, showing that common conflicts are successfully detected, and propose extension to a complete framework for efficiently recommending resolutions to medical conflicts in composed care pathway models.

Domain analysts, product managers, or customers aim to capture the important features and differences among a set of related products. A case-by-case reviewing of each product description is a laborious and time-consuming task that fails to deliver a condensed view of a product line. This paper introduces MatrixMiner: a tool for automatically synthesizing product comparison matrices (PCMs) from a set of product descriptions written in natural language. MatrixMiner is capable of identifying and organizing features and values in a PCM – despite the informality and absence of structure in the textual descriptions of products. Our empirical results of products mined from BestBuy show that the synthesized PCMs exhibit numerous quantitative, comparable information. Users can exploit MatrixMiner to visualize the matrix through a Web editor and review, refine, or complement the cell values thanks to the traceability with the original product descriptions and technical specifications.

In systems engineering, the deployment of software components is error-prone since numerous safety and security rules have to be preserved. Furthermore, many deployments on different heterogeneous platforms are possible. In this paper we present a technological solution to assist industrial practitioners in producing a safe and secure solution out of numerous architectural variants. First, we introduce a pattern technology that provides correct-by-construction deployment models through the reuse of modeling artifacts organized in a catalog. Second, we develop a variability solution, connected to the pattern technology and based on an extension of the common variability language, for supporting the synthesis of model-based architectural variants. This paper describes a live demonstration of an industrial effort seeking to bridge the gap between variability modeling and model-based systems engineering practices. We illustrate the tooling support with an industrial case study (a secure radio platform).

Product Derivation is a key activity in Software Product Line Engineering. During this process, derivation operators modify or create core assets (e.g., model elements, source code instructions, components) by adding, removing or substituting them according to a given configuration. The result is a derived product that generally needs to conform to a programming or modeling language. Some operators lead to invalid products when applied to certain assets, some others do not; knowing this in advance can help to better use them, however this is challenging, specially if we consider assets expressed in extensive and complex languages such as Java. In this paper, we empirically answer the following question: which product line operators, applied to which program elements, can synthesize variants of programs that are incorrect, correct or perhaps even conforming to test suites? We implement source code transformations, based on the derivation operators of the Common Variability Language. We automatically synthesize more than 370,000 program variants from a set of 8 real large Java projects (up to 85,000 lines of code), obtaining an extensive panorama of the sanity of the operations.

Model-based Software Product Line (MSPL) engineering aims at deriving customized models corresponding to individual products of a family. MSPL approaches usually promote the joint use of a variability model, a base model expressed in a specific formalism, and a realization layer that maps variation points to model elements. The design space of an MSPL is extremely complex to manage for the engineer, since the number of variants may be exponential and the derived product models have to be conformant to numerous well-formedness and business rules. In this paper, the objective is to provide a way to generate MSPLs, called counterexamples, that can produce invalid product models despite a valid configuration in the variability model. We provide a systematic and automated process, based on the Common Variability Language (CVL), to randomly search the space of MSPLs for a specific formalism. We validate the effectiveness of this process for three formalisms at different scales (up to 247 metaclasses and 684 rules). We also explore and discuss how counterexamples could guide practitioners when customizing derivation engines, when implementing checking rules that prevent early incorrect CVL models, or simply when specifying an MSPL.

Software Product Lines (SPLs) have evolved and gained attention as one of the most promising approaches for software reuse. Feature models are the main technique to represent domain variability in SPLs. However, there are other domain aspects, besides variability, which cannot be expressed in a feature model. Also, these diagrams were not designed to facilitate information retrieval, interoperability and inference. In contrast, ontologies seem to be the best solution to meet these requirements. Therefore, this work presents an approach for semantic enrichment of SPLs using ontologies. Our proposal provides methods to add domain information besides variability description, and a top-ontology that specifies generic concepts and relations in an SPL, working as a guide model for information addition. The proposed approach reuses the existing SPL feature model, adding semantic descriptions in a less intrusive way than modifying the feature model notation.