28 research outputs found
Two Approaches to Ontology Aggregation Based on Axiom Weakening
Axiom weakening is a novel technique that allows
for fine-grained repair of inconsistent ontologies.
In a multi-agent setting, integrating ontologies corresponding
to multiple agents may lead to inconsistencies.
Such inconsistencies can be resolved after
the integrated ontology has been built, or their
generation can be prevented during ontology generation.
We implement and compare these two approaches.
First, we study how to repair an inconsistent
ontology resulting from a voting-based aggregation
of views of heterogeneous agents. Second,
we prevent the generation of inconsistencies by letting
the agents engage in a turn-based rational protocol
about the axioms to be added to the integrated
ontology. We instantiate the two approaches using
real-world ontologies and compare them by measuring
the levels of satisfaction of the agents w.r.t.
the ontology obtained by the two procedures
A Bayesian Extension of the Description Logic ALC
Description logics (DLs) are well-known knowledge representation formalisms focused on the representation of terminological knowledge. A probabilistic extension of a light-weight DL was recently proposed for dealing with certain knowledge occurring in uncertain contexts. In this paper, we continue that line of research by introducing the Bayesian extension BALC of the DL ALC. We present a tableau-based procedure for deciding consistency, and adapt it to solve other probabilistic, contextual, and general inferences in this logic. We also show that all these problems remain ExpTime-complete, the same as reasoning in the underlying classical ALC
Ignoring Time Dependence in Software Engineering Data. A Mistake
Researchers often delve into the connections between different factors
derived from the historical data of software projects. For example, scholars
have devoted their endeavors to the exploration of associations among these
factors. However, a significant portion of these studies has failed to consider
the limitations posed by the temporal interdependencies among these variables
and the potential risks associated with the use of statistical methods
ill-suited for analyzing data with temporal connections. Our goal is to
highlight the consequences of neglecting time dependence during data analysis
in current research. We pinpointed out certain potential problems that arise
when disregarding the temporal aspect in the data, and support our argument
with both theoretical and real examples
The Probabilistic Description Logic BALC
Description logics (DLs) are well-known knowledge representation formalisms focused on the representation of terminological knowledge. Due to their first-order semantics, these languages (in their classical form) are not suitable for representing and handling uncertainty. A proba- bilistic extension of a light-weight DL was recently proposed for dealing with certain knowledge occurring in uncertain contexts. In this paper, we continue that line of research by introducing the Bayesian extension BALC of the propositionally closed DL ALC. We present a tableau-based procedure for deciding consistency and adapt it to solve other probabilistic, contextual, and gen- eral inferences in this logic. We also show that all these problems remain ExpTime-complete, the same as reasoning in the underlying classical ALC
Breaks and Code Quality: Investigating the Impact of Forgetting on Software Development. A Registered Report
Developers interrupting their participation in a project might slowly forget
critical information about the code, such as its intended purpose, structure,
the impact of external dependencies, and the approach used for implementation.
Forgetting the implementation details can have detrimental effects on software
maintenance, comprehension, knowledge sharing, and developer productivity,
resulting in bugs, and other issues that can negatively influence the software
development process. Therefore, it is crucial to ensure that developers have a
clear understanding of the codebase and can work efficiently and effectively
even after long interruptions. This registered report proposes an empirical
study aimed at investigating the impact of the developer's activity breaks
duration and different code quality properties. In particular, we aim at
understanding if the amount of activity in a project impact the code quality,
and if developers with different activity profiles show different impacts on
code quality. The results might be useful to understand if it is beneficial to
promote the practice of developing multiple projects in parallel, or if it is
more beneficial to reduce the number of projects each developer contributes
On the Empirical Evidence of Microservice Logical Coupling. A Registered Report
[Context] Coupling is a widely discussed metric by software engineers while
developing complex software systems, often referred to as a crucial factor and
symptom of a poor or good design. Nevertheless, measuring the logical coupling
among microservices and analyzing the interactions between services is
non-trivial because it demands runtime information in the form of log files,
which are not always accessible. [Objective and Method] In this work, we
propose the design of a study aimed at empirically validating the Microservice
Logical Coupling (MLC) metric presented in our previous study. In particular,
we plan to empirically study Open Source Systems (OSS) built using a
microservice architecture. [Results] The result of this work aims at
corroborating the effectiveness and validity of the MLC metric. Thus, we will
gather empirical evidence and develop a methodology to analyze and support the
claims regarding the MLC metric. Furthermore, we establish its usefulness in
evaluating and understanding the logical coupling among microservices
The Bayesian Description Logic BALC
Description Logics (DLs) that support uncertainty are not as well studied as their crisp alternatives, thereby limiting their use in real world domains. The Bayesian DL BEL and its extensions have been introduced to deal with uncertain knowledge without assuming (prob- abilistic) independence between axioms. In this paper we combine the classical DL ALC with Bayesian Networks. Our new DL includes a so- lution to the consistency checking problem and changes to the tableaux algorithm that are not a part of BEL. Furthermore, BALC also supports probabilistic assertional information which was not studied for BEL. We present algorithms for four categories of reasoning problems for our logic; two versions of concept satisfiability (referred to as total concept satis- fiability and partial concept satisfiability respectively), knowledge base consistency, subsumption, and instance checking. We show that all rea- soning problems in BALC are in the same complexity class as their classical variants, provided that the size of the Bayesian Network is included in the size of the knowledge base