PETS2009 and Winter-PETS 2009 results: a combined evaluation

This paper presents the results of the crowd image analysis challenge of the Winter PETS 2009 workshop. The evaluation is carried out using a selection of the metrics developed in the Video Analysis and Content Extraction (VACE) program and the CLassification of Events, Activities, and Relationships (CLEAR) consortium [13]. The evaluation highlights the detection and tracking performance of the authors’systems in areas such as precision, accuracy and robustness. The performance is also compared to the PETS 2009 submitted results

Why and How Your Traceability Should Evolve: Insights from an Automotive Supplier

Traceability is a key enabler of various activities in automotive software and systems engineering and required by several standards. However, most existing traceability management approaches do not consider that traceability is situated in constantly changing development contexts involving multiple stakeholders. Together with an automotive supplier, we analyzed how technology, business, and organizational factors raise the need for flexible traceability. We present how traceability can be evolved in the development lifecycle, from early elicitation of traceability needs to the implementation of mature traceability strategies. Moreover, we shed light on how traceability can be managed flexibly within an agile team and more formally when crossing team borders and organizational borders. Based on these insights, we present requirements for flexible tool solutions, supporting varying levels of data quality, change propagation, versioning, and organizational traceability.Comment: 9 pages, 3 figures, accepted in IEEE Softwar

Probabilistic modeling of texture transition for fast tracking and delineation

In this thesis a probabilistic approach to texture boundary detection for tracking applications is presented. We have developed a novel fast algorithm for Bayesian estimation of texture transition locations from a short sequence of pixels on a scanline that combines the desirable speed of edge-based line search and the sophistication of Bayesian texture analysis given a small set of observations. For the cases where the given observations are too few for reliable Bayesian estimation of probability of texture change we propose an innovative machine learning technique to generate a probabilistic texture transition model. This is achieved by considering a training dataset containing small patches of blending textures. By encompassing in the training set enough examples to accurately model texture transitions of interest we can construct a predictor that can be used for object boundary tracking that can deal with few observations and demanding cases of tracking of arbitrary textured objects against cluttered background. Object outlines are then obtained by combining the texture crossing probabilities across a set of scanlines. We show that a rigid geometric model of the object to be tracked or smoothness constraints in the absence of such a model can be used to coalesce the scanline texture crossing probabilities obtained using the methods mentioned above. We propose a Hidden Markov Model to aggregate robustly the sparse transition probabilities of scanlines sampled along the projected hypothesis model contour. As a result continuous object contours can be extracted using a posteriori maximization of texture transition probabilities. On the other hand, stronger geometric constraints such as available rigid models of the target are directly enforced by robust stochastic optimization. In addition to being fast, the allure of the proposed probabilistic framework is that it accommodates a unique infrastructure for tracking of heterogeneous objects which utilizes the machine learning-based predictor as well as the Bayesian estimator interchangeably in conjunction with robust optimization to extract object contours robustly. We apply the developed methods to tracking of textured and non textured rigid objects as well as deformable body outlines and monocular articulated human motion in challenging conditions. Finally, because it is fast, our method can also serve as an interactive texture segmentation tool

Software Robustness: From Requirements to Verification

The importance of software quality increases as software products become more intertwined with our everyday lives. A critical software quality attribute is robustness, i.e. that the software shows stable behavior in stressful conditions and when receiving faulty inputs. Even though this has been a long-term goal in software engineering, few studies directly target robustness. The overallgoal of this thesis is to identify gaps in the knowledge and take steps towards improving and creating methods to work with software robustness.To identify gaps in the state of knowledge, this thesis first describes a systematic review of the academic literature on software robustness. The results, based on analysis of 144 relevant papers, suggest that the most prominent contributions on robustness are methods and tools for random testing on the external interfaces of systems. Another finding is the lack of empirical evidence and guidelines on how to dene and specify robustness. Additionally, there is a lack of methods to elicit, analyze, and specify robustness requirements in a systematic way, and to test these requirements.To address the goals of the thesis, we have worked with ve industrial companies. We examined the state of practice by conducting interviews and analyzing requirements documents at some of our partner companies to identify improvement potential. The results show that there also is a lack of systematic methods to specify and test quality requirements in practice. Furthermore, unverifiable quality requirements are still a source of problem and high cost to software development projects.To address these issues, we constructed a framework for analysis, elicitation, and specification of software robustness (ROAST). Based on simple models for root causes and symptoms of robustness failures, we have identified19 patterns for robustness requirements. Further, ROAST includes a notion of specification levels that helps practitioners refine high-level requirements toa verifiable level. The framework has been evaluated using document analysis, interviews, and surveys at the partner companies. The evaluations have investigated the usefulness, quality, and generalizability of ROAST and havehelped us improve the framework over time.The last part of the thesis uses the patterns in ROAST, to specify generic robustness properties that the system should fulfill. We present a testing framework, RobusTest, that uses these properties to automatically generate robustness test cases. This provides a more focused testing than complete random testing. We have implemented and evaluated parts of this framework and found robustness issues in open source and well-tested industrial systems.This thesis provides guidelines for and discusses how practitioners can more systematically work with robustness from requirements elicitation and analysis to testing

A Framework for Specifying Software Robustness Requirements

The annual town report for Sockholm Maine. Organized March 23, 1895. Incorporated as a town, 1911

RobusTest: Towards a Framework for Automated Testing of Robustness in Software

Growing complexity of software systems and increasingdemand for higher quality systems has resulted in more focus on software robustness in academia and research.By increasing the robustness of a software many failures which decrease the quality of the system can be avoided or masked. When it comes to specification, testing and assessing software robustness in an efficient manner the methods and techniques are not mature yet.This paper presents the idea of a framework RobusTest fortesting robustness properties of a system with focus on timing issues. The test cases provided by the framework are formulated as properties with strong links to robustness requirements. These requirements are categorized into patterns as specified in the ROAST framework for specifying and eliciting robustness requirements. The properties are then used for automatically generating robustness test cases and assessing the results

RobusTest: A Framework for Automated Testing of Software Robustness

Robustness of a software system is defined as the degree to which the system can behave ordinarily and in conformance with the requirements in extraordinary situations. By increasing the robustness many failures which decrease the quality of the system can be avoided or masked. When it comes to specifying, testing and assessing software robustness in an efficient manner the methods and techniques are not mature yet. This paper presents RobusTest, a framework for testing robustness properties of a system with currently focus on timing issues. The expected robust behavior of the system is formulated as properties. The properties are then used to automatically generate robustness test cases and assess the results. An implementation of RobusTest in Java is presented here together with results from testing different, open-source implementations of the XMPP instant messaging protocol. By executing 400 test cases that were automatically generated from properties on two such implementations we found 11 critical failures and 15 nonconformance problems as compared to the XMPP specification