388 research outputs found
Coverage Protocols for Wireless Sensor Networks: Review and Future Directions
The coverage problem in wireless sensor networks (WSNs) can be generally
defined as a measure of how effectively a network field is monitored by its
sensor nodes. This problem has attracted a lot of interest over the years and
as a result, many coverage protocols were proposed. In this survey, we first
propose a taxonomy for classifying coverage protocols in WSNs. Then, we
classify the coverage protocols into three categories (i.e. coverage aware
deployment protocols, sleep scheduling protocols for flat networks, and
cluster-based sleep scheduling protocols) based on the network stage where the
coverage is optimized. For each category, relevant protocols are thoroughly
reviewed and classified based on the adopted coverage techniques. Finally, we
discuss open issues (and recommend future directions to resolve them)
associated with the design of realistic coverage protocols. Issues such as
realistic sensing models, realistic energy consumption models, realistic
connectivity models and sensor localization are covered
Evolutionary Search Techniques with Strong Heuristics for Multi-Objective Feature Selection in Software Product Lines
Software design is a process of trading off competing objectives. If the user objective space is rich, then we should use optimizers that can fully exploit that richness. For example, this study configures software product lines (expressed as feature models) using various search-based software engineering methods. Our main result is that as we increase the number of optimization objectives, the methods in widespread use (e.g. NSGA-II, SPEA2) perform much worse than IBEA (Indicator-Based Evolutionary Algorithm). IBEA works best since it makes most use of user preference knowledge. Hence it does better on the standard measures (hypervolume and spread) but it also generates far more products with 0 violations of domain constraints. We also present significant improvements to IBEA\u27s performance by employing three strong heuristic techniques that we call PUSH, PULL, and seeding. The PUSH technique forces the evolutionary search to respect certain rules and dependencies defined by the feature models, while the PULL technique gives higher weight to constraint satisfaction as an optimization objective and thus achieves a higher percentage of fully-compliant configurations within shorter runtimes. The seeding technique helps in guiding very large feature models to correct configurations very early in the optimization process. Our conclusion is that the methods we apply in search-based software engineering need to be carefully chosen, particularly when studying complex decision spaces with many optimization objectives. Also, we conclude that search methods must be customized to fit the problem at hand. Specifically, the evolutionary search must respect domain constraints
Replan: Release planning for agile development
Release Planning methodologies have made possible that project managers
and users in general can plan project’s releases. These methods try
to automatize the human-based planning processes. Currently they are a
few web-based and stand-alone tools about release planning, but not all of
them offer the same functionalities, like the update of an already planned
release or a detailed plan expressed in a timeline. Moreover, these systems
are oriented to stakeholders criteria, without taking enough consideration
to the available resources. This becomes a limitation, because in many occasions
it is vital to have a temporal planning of a release. It also affects key
aspects like the planning efficiency or the speed at which it is executed.
In this project a web-based release planning tool has been developed.
In this tool, users can create a release with different entities in an easy and
simple way. The tool is based in a mathematical model that generates an
scheduled plan as tight as possible to the available time and resources. On
the other hand, the tool also guarantees the priority fulfillment of features,
by respecting the temporal criteria that the user could establish.
The system is also modular, as it can be integrated with other possible
different visualizations. Its development in a cloud server also provides
public access and scalability.
The tests performed to the system show that the presented mathematical
model guarantees the scheduled and efficient planning of a project’s
release
Empirical Evaluation of Mutation-based Test Prioritization Techniques
We propose a new test case prioritization technique that combines both
mutation-based and diversity-based approaches. Our diversity-aware
mutation-based technique relies on the notion of mutant distinguishment, which
aims to distinguish one mutant's behavior from another, rather than from the
original program. We empirically investigate the relative cost and
effectiveness of the mutation-based prioritization techniques (i.e., using both
the traditional mutant kill and the proposed mutant distinguishment) with 352
real faults and 553,477 developer-written test cases. The empirical evaluation
considers both the traditional and the diversity-aware mutation criteria in
various settings: single-objective greedy, hybrid, and multi-objective
optimization. The results show that there is no single dominant technique
across all the studied faults. To this end, \rev{we we show when and the reason
why each one of the mutation-based prioritization criteria performs poorly,
using a graphical model called Mutant Distinguishment Graph (MDG) that
demonstrates the distribution of the fault detecting test cases with respect to
mutant kills and distinguishment
Geometric Particle Swarm Optimization for Multi-objective Optimization Using Decomposition
Multi-objective evolutionary algorithms (MOEAs) based on decomposition are aggregation-based algorithms which transform a multi-objective optimization problem (MOP) into several single-objective subproblems. Being effective, efficient, and easy to implement, Particle Swarm Optimization (PSO) has become one of the most popular single-objective optimizers for continuous problems, and recently it has been successfully extended to the multi-objective domain. However, no investigation on the application of PSO within a
multi-objective decomposition framework exists in the context of combinatorial optimization. This is precisely the focus of the paper. More specifically, we study the incorporation of Geometric Particle Swarm Optimization (GPSO), a discrete generalization of PSO that has proven successful on a number of single-objective combinatorial problems, into a decomposition approach. We conduct experiments on manyobjective 1/0 knapsack problems i.e. problems with more
than three objectives functions, substantially harder than multi-objective problems with fewer objectives. The results indicate that the proposed multi-objective GPSO based on decomposition is able to outperform two version of the wellknow MOEA based on decomposition (MOEA/D) and the
most recent version of the non-dominated sorting genetic algorithm (NSGA-III), which are state-of-the-art multi-objective evolutionary approaches based on decomposition
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