162,316 research outputs found
Combinatorial-Based Prioritization for User-Session-Based Test Suites
Software defects caused by inadequate software testing can cost billions of dollars. Further, web application defects can be costly due to the fact that most web applications handle constant user interaction. However, software testing is often under time and budget constraints. By improving the time efficiency of software testing, many of the costs associated with defects can be saved. Current methods for web application testing can take too long to generate test suites. In addition, studies have shown that user-session-based test suites often find faults missed by other testing techniques. This project addresses this problem by utilizing existing user sessions for web application testing. The software testing method provided within this project utilizes previous knowledge about combinatorial coverage testing and improves time and computer memory efficiency by only considering test cases that exist in a user-session based test suite. The method takes the existing test suite and prioritizes the test cases based on a specific combinatorial criterion. In addition, this project presents an empirical study examining the application of the newly proposed combinatorial prioritization algorithm on an existing web application
How Time-Fault Ratio helps in Test Case Prioritization for Regression Testing
Regression testing analyzes whether the maintenance of the software has adversely affected its normal functioning. Regression testing is generally performed under the strict time constraints. Due to limited time budget, it is not possible to test the software with all available test cases. Thus, the reordering of the test cases, on the basis of their effectiveness, is always needed. A test prioritization technique, which prioritizes the test cases on the basis of their Time -Fault Ratio (TFR), has been proposed in this paper. The technique tends to maximize the fault detection as the faults are exposed in the ascending order of their detection times. The proposed technique may be used at any stage of software development
SANTO: Social Aerial NavigaTion in Outdoors
In recent years, the advances in remote connectivity, miniaturization of electronic components and computing power has led to the integration of these technologies in daily devices like cars or aerial vehicles. From these, a consumer-grade option that has gained popularity are the drones or unmanned aerial vehicles, namely quadrotors. Although until recently they have not been used for commercial applications, their inherent potential for a number of tasks where small and intelligent devices are needed is huge. However, although the integrated hardware has advanced exponentially, the refinement of software used for these applications has not beet yet exploited enough. Recently, this shift is visible in the improvement of common tasks in the field of robotics, such as object tracking or autonomous navigation. Moreover, these challenges can become bigger when taking into account the dynamic nature of the real world, where the insight about the current environment is constantly changing. These settings are considered in the improvement of robot-human interaction, where the potential use of these devices is clear, and algorithms are being developed to improve this situation. By the use of the latest advances in artificial intelligence, the human brain behavior is simulated by the so-called neural networks, in such a way that computing system performs as similar as possible as the human behavior. To this end, the system does learn by error which, in an akin way to the human learning, requires a set of previous experiences quite considerable, in order for the algorithm to retain the manners. Applying these technologies to robot-human interaction do narrow the gap. Even so, from a bird's eye, a noticeable time slot used for the application of these technologies is required for the curation of a high-quality dataset, in order to ensure that the learning process is optimal and no wrong actions are retained. Therefore, it is essential to have a development platform in place to ensure these principles are enforced throughout the whole process of creation and optimization of the algorithm. In this work, multiple already-existing handicaps found in pipelines of this computational gauge are exposed, approaching each of them in a independent and simple manner, in such a way that the solutions proposed can be leveraged by the maximum number of workflows. On one side, this project concentrates on reducing the number of bugs introduced by flawed data, as to help the researchers to focus on developing more sophisticated models. On the other side, the shortage of integrated development systems for this kind of pipelines is envisaged, and with special care those using simulated or controlled environments, with the goal of easing the continuous iteration of these pipelines.Thanks to the increasing popularity of drones, the research and development of autonomous capibilities has become easier. However, due to the challenge of integrating multiple technologies, the available software stack to engage this task is restricted. In this thesis, we accent the divergencies among unmanned-aerial-vehicle simulators and propose a platform to allow faster and in-depth prototyping of machine learning algorithms for this drones
Partition strategies for incremental Mini-Bucket
Los modelos en grafo probabilÃsticos, tales como los campos aleatorios de
Markov y las redes bayesianas, ofrecen poderosos marcos de trabajo para la
representación de conocimiento y el razonamiento en modelos con gran número
de variables. Sin embargo, los problemas de inferencia exacta en modelos de
grafos son NP-hard en general, lo que ha causado que se produzca bastante
interés en métodos de inferencia aproximados.
El mini-bucket incremental es un marco de trabajo para inferencia aproximada
que produce como resultado lÃmites aproximados inferior y superior de la
función de partición exacta, a base de -empezando a partir de un modelo con
todos los constraints relajados, es decir, con las regiones más pequeñas posibleincrementalmente
añadir regiones más grandes a la aproximación. Los métodos
de inferencia aproximada que existen actualmente producen lÃmites superiores
ajustados de la función de partición, pero los lÃmites inferiores suelen ser demasiado
imprecisos o incluso triviales.
El objetivo de este proyecto es investigar estrategias de partición que mejoren
los lÃmites inferiores obtenidos con el algoritmo de mini-bucket, trabajando dentro
del marco de trabajo de mini-bucket incremental.
Empezamos a partir de la idea de que creemos que deberÃa ser beneficioso
razonar conjuntamente con las variables de un modelo que tienen una alta correlación,
y desarrollamos una estrategia para la selección de regiones basada en
esa idea. Posteriormente, implementamos nuestra estrategia y exploramos formas
de mejorarla, y finalmente medimos los resultados obtenidos usando nuestra
estrategia y los comparamos con varios métodos de referencia.
Nuestros resultados indican que nuestra estrategia obtiene lÃmites inferiores
más ajustados que nuestros dos métodos de referencia. También consideramos
y descartamos dos posibles hipótesis que podrÃan explicar esta mejora.Els models en graf probabilÃstics, com bé els camps aleatoris de Markov i les
xarxes bayesianes, ofereixen poderosos marcs de treball per la representació
del coneixement i el raonament en models amb grans quantitats de variables.
Tanmateix, els problemes d’inferència exacta en models de grafs son NP-hard
en general, el qual ha provocat que es produeixi bastant d’interès en mètodes
d’inferència aproximats.
El mini-bucket incremental es un marc de treball per a l’inferència aproximada
que produeix com a resultat lÃmits aproximats inferior i superior de la
funció de partició exacta que funciona començant a partir d’un model al qual
se li han relaxat tots els constraints -és a dir, un model amb les regions més
petites possibles- i anar afegint a l’aproximació regions incrementalment més
grans. Els mètodes d’inferència aproximada que existeixen actualment produeixen
lÃmits superiors ajustats de la funció de partició. Tanmateix, els lÃmits
inferiors acostumen a ser massa imprecisos o fins aviat trivials.
El objectiu d’aquest projecte es recercar estratègies de partició que millorin
els lÃmits inferiors obtinguts amb l’algorisme de mini-bucket, treballant dins del
marc de treball del mini-bucket incremental.
La nostra idea de partida pel projecte es que creiem que hauria de ser beneficiós
per la qualitat de l’aproximació raonar conjuntament amb les variables del
model que tenen una alta correlació entre elles, i desenvolupem una estratègia
per a la selecció de regions basada en aquesta idea. Posteriorment, implementem
la nostra estratègia i explorem formes de millorar-la, i finalment mesurem els
resultats obtinguts amb la nostra estratègia i els comparem a diversos mètodes
de referència.
Els nostres resultats indiquen que la nostra estratègia obté lÃmits inferiors
més ajustats que els nostres dos mètodes de referència. També considerem i
descartem dues possibles hipòtesis que podrien explicar aquesta millora.Probabilistic graphical models such as Markov random fields and Bayesian networks
provide powerful frameworks for knowledge representation and reasoning
over models with large numbers of variables. Unfortunately, exact inference
problems on graphical models are generally NP-hard, which has led to signifi-
cant interest in approximate inference algorithms.
Incremental mini-bucket is a framework for approximate inference that provides
upper and lower bounds on the exact partition function by, starting from
a model with completely relaxed constraints, i.e. with the smallest possible
regions, incrementally adding larger regions to the approximation. Current
approximate inference algorithms provide tight upper bounds on the exact partition
function but loose or trivial lower bounds.
This project focuses on researching partitioning strategies that improve the
lower bounds obtained with mini-bucket elimination, working within the framework
of incremental mini-bucket.
We start from the idea that variables that are highly correlated should be
reasoned about together, and we develop a strategy for region selection based
on that idea. We implement the strategy and explore ways to improve it, and
finally we measure the results obtained using the strategy and compare them to
several baselines.
We find that our strategy performs better than both of our baselines. We
also rule out several possible explanations for the improvement
Project Success in Agile Development Projects
The paper explains and clarifies the differences between Waterfall and Agile
development methodologies, establishes what criteria could be taken into
account to properly define project success within the scope of software
development projects, and finally tries to clarify if project success is the
reason why many organizations are moving to Agile methodologies from other ones
such as Waterfall. In the form of a literature review, it analyses several,
publications, investigations and case studies that point out the motives why
companies moved to Agile, as well as the results they observed afterward. It
also analyses overall statistics of project outcomes after companies evolved
from traditional methodologies such as Waterfall to Agile development
approaches
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