Gabriella Castro Barbosa Costa

A Scientific Software Product Line for the Bioinformatics domain

CONTEXT Most specialized users (scientists) that use bioinformatics applications do not have suitable training on software development. Software Product Line (SPL) employs the concept of reuse considering that it is defined as a set of systems that are developed from a common set of base artifacts. In some contexts, such as in bioinformatics applications, it is advantageous to develop a collection of related software products, using SPL approach. If software products are similar enough, there is the possibility of predicting their commonalities, differences and then reuse these common features to support the development of new applications in the bioinformatics area. OBJECTIVES This paper presents the PL-Science approach which considers the context of SPL and ontology in order to assist scientists to define a scientific experiment, and to specify a workflow that encompasses bioinformatics applications of a given experiment. This paper also focuses on the use of ontologies to enable the use of Software Product Line in biological domains. METHOD In the context of this paper, Scientific Software Product Line (SSPL) differs from the Software Product Line due to the fact that SSPL uses an abstract scientific workflow model. This workflow is defined according to a scientific domain and using this abstract workflow model the products (scientific applications/algorithms) are instantiated. RESULTS Through the use of ontology as a knowledge representation model, we can provide domain restrictions as well as add semantic aspects in order to facilitate the selection and organization of bioinformatics workflows in a Scientific Software Product Line. The use of ontologies enables not only the expression of formal restrictions but also the inferences on these restrictions, considering that a scientific domain needs a formal specification. CONCLUSIONS This paper presents the development of the PL-Science approach, encompassing a methodology and an infrastructure, and also presents an approach evaluation. This evaluation presents case studies in bioinformatics, which were conducted in two renowned research institutions in Brazil.

From applications to a software ecosystem platform: an exploratory study

The essence of the software ecosystem concept encourages external developers to interact with a software platform, enabling them to contribute or evolve such platform. Trying to expand a product into a platform on which components, services and applications can be created, a company may provide the basis for an ecosystem and its life cycle. In this paper, we describe the evolution process of an application towards an ecosystem using current models and taking into account the reuse of an existing software system. The application DiaDia has been developed for smartphones and its main function consists in helping the treatment of diabetic patients. Two different models are used to discuss the transition of the application to an ecosystem: one is an adaptation of the Software Supply Network which represents the main actors that interact with the ecosystem, and the other covers the process of creating an application and its evolution to an ecosystem through Business Process Model and Notation.

PL-Science: A Scientific Software Product Line☆

Abstract A way to improve reusability and maintainability of a family of software products is through the use of Software Product Line (SPL) approach. Software families, also named SPLs, are a set of software intensive systems sharing a common set of features which are managed to satisfy specific needs of a particular market segment or mission and that are developed from a common set of core assets in a prescribed way. This paper presents the PL-Science approach that considers the context of SPL and aims to assist scientists to define a scientific experiment, specifying a workflow that encompasses scientific applications of a given experiment. Using SPL concepts, scientists can reuse models that specify the scientific product line, and carefully can make decisions according to their needs. In the context of this paper, Scientific Software Product Lines (SSPL) differs from the Software Product Lines (SPL) due to the fact that SSPL uses an abstract scientific workflow model. This workflow is defined according to a scientific domain and, using this abstract workflow model, the products (scientific applications/algorithms) will be instantiated. This paper also focuses on the use of ontologies to facilitate the process of applying Software Product Line (SPL) to scientific domains. Through the use of ontology as a domain model, we can provide additional information as well as add more semantics in the context of Scientific Software Product Lines (SSPL).

Using data provenance to improve software process enactment, monitoring and analysis

A practice to support software processes continuous improvement is to reuse the knowledge acquired in previous executions. One way to capture process execution data is by using data provenance models. Data provenance refers to the origin, lineage or source of data. In computational terms, provenance is a historical record of the derivation of data that can help in its understanding. But, does the provenance data be used to contribute to the software process improvement? Based on this question, the approach proposed in this work aims to apply data provenance to support software process execution, monitoring and analysis phases as well as software process improvement as a whole. To achieve this goal, data provenance, ontology and predefined metrics were used in a pilot case study, considering software processes used in two real software development companies. With this study, two types of implicit information were derived: (1) information about artifacts that can increase new process instances runtime, and (2) information related to new agents that can be added to execute a task and contribute to the reduction of the task runtime.

Software Process Performance Improvement Using Data Provenance and Ontology

Organizations are investing on process definition and improvement in order to enhance their products’ quality. In the software processes context, this is not different. A practice to support software processes continuous improvement is to reuse the knowledge acquired in previous process executions. After defining measures to the software processes performance, an analysis of process execution data can be done, in order to detect process enhancement points. One way to capture these process execution data is using data provenance models. Thus, these data can be analyzed, using information derivation mechanisms, such as inference engines for ontologies. This paper aims to describe and evaluate an approach to support software process execution analysis to improve process performance, using data provenance and ontologies. A pilot case study was conducted with software processes used in two software development companies. With this study, implicit information was derived and can be used for improving process performance.

Monitoring Collaboration in Software Processes Using Social Networks

Collaboration monitoring in software process is important to check if the collaboration is indeed happening as planned, but there are few approaches that define how to measure and monitor collaboration. By assessing collaboration during an ongoing process execution, project managers can take corrective actions that might improve the process execution and, consequently, reflect on quality gains of the final product. This research work proposes to evaluate the level of coordination achieved by a running software process through social network analysis metrics.