Vincenzo Ferme

Investigating the impact of code smells debt on quality code evaluation

Different forms of technical debt exist that have to be carefully managed. In this paper we focus our attention on design debt, represented by code smells. We consider three smells that we detect in open source systems of different domains. Our principal aim is to give advice on which design debt has to be paid first, according to the three smells we have analyzed. Moreover, we discuss if the detection of these smells could be tailored to the specific application domain of a system.

A Framework for Benchmarking BPMN 2.0 Workflow Management Systems

The diverse landscape of Workflow Management Systems WfMSs makes it challenging for users to compare different solutions to identify the ones most suitable to their requirements. Thus a comparison framework that would define common grounds in many different aspects, such as price, reliability, security, robustness and performance is necessary. In this paper we focus on the performance aspect, and we present a framework for automatic and reliable calculation of performance metrics for BPMN 2.0 WfMSs. We validate the framework by applying it on two open-source WfMSs. The goal is to contribute to the improvement of existing WfMSs by pinpointing performance bottlenecks, and to empower end users to make informed decisions when selecting a WfMS.

On the Road to Benchmarking BPMN 2.0 Workflow Engines

Workflow Management Systems (WfMSs) provide platforms for delivering complex service-oriented applications that need to satisfy enterprise-grade quality of service requirements such as dependability and scalability. In this paper we focus on the case of benchmarking the performance of the core of WfMSs, Workflow Engines, that are compliant with the Business Process Model and Notation 2.0 (BPMN 2.0) standard. We first explore the main challenges that need to be met when designing such a benchmark and describe the approaches we designed for tackling them in the BenchFlow project. We discuss our approach to distill the essence of real-world processes to create from it processes for the benchmark, and to ensure that the benchmark finds wide applicability.

Towards assessing software architecture quality by exploiting code smell relations

We can evaluate software architecture quality using a plethora of metrics proposed in the literature, but interpreting and exploiting in the right way these metrics is not always a simple task. This is true for both fixing the right metric threshold values and determining the actions to be taken to improve the quality of the system. Instead of metrics, we can detect code or architectural anomalies that give us useful hints on the possible architecture degradation. In this paper, we focus our attention on the detection of code smells and in particular on their relations and co-occurrences, with the aim to evaluate technical debt in an architectural context. We start from the assumption that certain patterns of code anomalies tend to be better indicators of architectural degradation than simple metrics evaluation.

Towards a prioritization of code debt: A code smell Intensity Index

Code smells can be used to capture symptoms of code decay and potential maintenance problems that can be avoided by applying the right refactoring. They can be seen as a source of technical debt. However, tools for code smell detection often provide far too many and different results, and identify many false positive code smell instances. In fact, these tools are rooted on initial and rather informal code smell definitions. This represents a challenge to interpret their results in different ways. In this paper, we provide an Intensity Index, to be used as an estimator to determine the most critical instances, prioritizing the examination of smells and, potentially, their removal. We apply Intensity on the detection of six well known and common smells and we report their Intensity distribution from an analysis performed on 74 systems of the Qualitas Corpus, showing how Intensity could be used to prioritize code smells inspection.

Investigating the Impact of Code Smells on System's Quality: An Empirical Study on Systems of Different Application Domains

There are various activities that support software maintenance. Program comprehension and detection of design anomalies and their symptoms, like code smells and anti patterns, are particularly relevant for improving the quality and facilitating evolution of a system. In this paper we describe an empirical study on the detection of code smells, aiming at identifying the most frequent smells in systems of different domains and hence the domains characterized by more smells. Moreover, we study possible correlations existing among smells and the values of a set of software quality metrics using Spearmans rank correlation and Principal Component Analysis.

Micro-Benchmarking BPMN 2.0 Workflow Management Systems with Workflow Patterns

Although Workflow Management Systems (WfMSs) are a key component in workflow technology, research work for assessing and comparing their performance is limited. This work proposes the first micro-benchmark for WfMSs that can execute BPMN 2.0 workflows. To this end, we focus on studying the performance impact of well-known workflow patterns expressed in BPMN 2.0 with respect to three open source WfMSs. We executed all the experiments under a reliable environment and produced a set of meaningful metrics. This paper contributes to the area of workflow technology by defining building blocks for more complex BPMN 2.0 WfMS benchmarks. The results have shown bottlenecks on architectural design decisions, resource utilization, and limits on the load a WfMS can sustain, especially for the cases of complex and parallel structures. Experiments on a mix of workflow patterns indicated that there are no unexpected performance side effects when executing different workflow patterns concurrently, although the duration of the individual workflows that comprised the mix was increased.

ABSTAT: Linked Data Summaries with ABstraction and STATistics

While much work has focused on continuously publishing Linked Open Data, little work considers how to help consumers to better understand existing datasets. ABSTAT framework aims at providing a better understanding of big and complex datasets by extracting summaries of linked data sets based on an ontology-driven data abstraction model. Our ABSTAT framework takes as input a data set and an ontology and returns an ontology-driven data summary as output. The summary is exported into RDF and then made accessible through a SPARQL endpoint and a web interface to support the navigation.

A Container-centric Methodology for Benchmarking Workflow Management Systems

Trusted benchmarks should provide reproducible results obtained following a transparent and well-defined process. In this paper, we show how Containers, originally developed to ease the automated deployment of Cloud application components, can be used in the context of a benchmarking methodology. The proposed methodology focuses on Workflow Management Systems (WfMSs), a critical service orchestration middleware, which can be characterized by its architectural complexity, for which Docker Containers offer a highly suitable approach. The contributions of our work are: 1) a new benchmarking approach taking full advantage of containerization technologies; and 2) the formalization of the interaction process with the WfMS vendors described clearly in a written agreement. Thus, we take advantage of emerging Cloud technologies to address technical challenges, ensuring the performance measurements can be trusted. We also make the benchmarking process transparent, automated, and repeatable so that WfMS vendors can join the benchmarking effort.

Poster: Filtering Code Smells Detection Results

Many tools for code smell detection have been developed, providing often different results. This is due to the informal definition of code smells and to the subjective interpretation of them. Usually, aspects related to the domain, size, and design of the system are not taken into account when detecting and analyzing smells. These aspects can be used to filter out the noise and achieve more relevant results. In this paper, we propose different filters that we have identified for five code smells. We provide two kind of filters, Strong and Weak Filters, that can be integrated as part of a detection approach.