Property templates and assertions supporting runtime failure detection

In the context of our research program we addressed the question whether or not requirements documents contain information about system level properties that can be exploited to automatically create assertions for run-time checks of these properties. In this technical report we define the concept of property template and report details on the studies we carried out to address this question. The results are presented in the form of a catalog of property templates, and details from the individual studies showing which properties occur in which projects

Achieving Cost-Effective Software Reliability Through Self-Healing

Heterogeneity, mobility, complexity and new application domains raise new software reliability issues that cannot be met cost-effectively only with classic software engineering approaches. Self-healing systems can successfully address these problems, thus increasing software reliability while reducing maintenance costs. Self-healing systems must be able to automatically identify runtime failures, locate faults, and find a way to bring the system back to an acceptable behavior. This paper discusses the challenges underlying the construction of self-healing systems with particular focus on functional failures, and presents a set of techniques to build software systems that can automatically heal such failures. It introduces techniques to automatically derive assertions to effectively detect functional failures, locate the faults underlying the failures, and identify sequences of actions alternative to the failing sequence to bring the system back to an acceptable behavior

A MDD Strategy for developing Context-Aware Pervasive Systems

This master thesis proposes a methodological approach to develop context-aware pervasive systems based on ontologies and the Model-Driven Development (MDD) guidelines.Serral Asensio, E. (2008). A MDD Strategy for developing Context-Aware Pervasive Systems. http://hdl.handle.net/10251/12446Archivo delegad

Image-Driven Automated End-to-End Testing for Mobile Applications

The increasing complexity and demand of software systems and the greater availability of test automation software is quickly rendering manual end-to-end (E2E) testing techniques for mobile platforms obsolete. This research seeks to explore the potential increase in automated test efficacy and maintainability through the use of computer vision algorithms when applied with Appium, a leading cross-platform mobile test automation framework. A testing framework written in a Node.js environment was created to support the development of E2E test scripts that examine and report the functional capabilities of a mobile test app. The test framework provides a suite of functions that connect with an Appium server and provide interaction with the mobile test app to perform actions and assertions like clicking and verifying text. To do this without modifying the test app source code, the system employs image templates representing specific app components and identifies them within the test app by utilizing feature detection, matching, and filtering. From experimentation on three test scripts across multiple iOS and Android device simulators, iOS test script runs had a pass rate of 38% on average, while Android test runs had a pass rate of 74.5% on average. The test scripts ran perfectly only on the device simulators from which the template images were extracted via screenshots, while failures were mostly due to invalid or mismatched templates. Therefore, more generic templates that appeal to a variety of different device renderings are necessary for the test framework to be completely reliable

3D Object Detection for Autonomous Driving: A Survey

Autonomous driving is regarded as one of the most promising remedies to shield human beings from severe crashes. To this end, 3D object detection serves as the core basis of such perception system especially for the sake of path planning, motion prediction, collision avoidance, etc. Generally, stereo or monocular images with corresponding 3D point clouds are already standard layout for 3D object detection, out of which point clouds are increasingly prevalent with accurate depth information being provided. Despite existing efforts, 3D object detection on point clouds is still in its infancy due to high sparseness and irregularity of point clouds by nature, misalignment view between camera view and LiDAR bird's eye of view for modality synergies, occlusions and scale variations at long distances, etc. Recently, profound progress has been made in 3D object detection, with a large body of literature being investigated to address this vision task. As such, we present a comprehensive review of the latest progress in this field covering all the main topics including sensors, fundamentals, and the recent state-of-the-art detection methods with their pros and cons. Furthermore, we introduce metrics and provide quantitative comparisons on popular public datasets. The avenues for future work are going to be judiciously identified after an in-deep analysis of the surveyed works. Finally, we conclude this paper.Comment: 3D object detection, Autonomous driving, Point cloud

Flexibility Support for Homecare Applications Based on Models and Multi-Agent Technology

In developed countries, public health systems are under pressure due to the increasing percentage of population over 65. In this context, homecare based on ambient intelligence technology seems to be a suitable solution to allow elderly people to continue to enjoy the comforts of home and help optimize medical resources. Thus, current technological developments make it possible to build complex homecare applications that demand, among others, flexibility mechanisms for being able to evolve as context does (adaptability), as well as avoiding service disruptions in the case of node failure (availability). The solution proposed in this paper copes with these flexibility requirements through the whole life-cycle of the target applications: from design phase to runtime. The proposed domain modeling approach allows medical staff to design customized applications, taking into account the adaptability needs. It also guides software developers during system implementation. The application execution is managed by a multi-agent based middleware, making it possible to meet adaptation requirements, assuring at the same time the availability of the system even for stateful applications.This work was financed in part by the University of the Basque Country (UPV/EHU) under project UFI 11/28, by the Regional Government of the Basque Country under Project IT719-13, and by the MCYT&FEDER under project DPI 2012-37806-C02-01

Improved software verification through program path-based analysis

This thesis describes the generation and use of program invariants to improve software reliability. It introduces PRECIS, a technique for automatic invariant generation based on program path guided clustering. The invariants generated by PRECIS can be directly used by programmers for regression testing and improved code documentation. The generated invariants can also be used as part of hardware error detectors, by checking variables key to program output. PREAMBL, a bug localization technique, is introduced as away of providing increased utility to the generated invariants in diagnosing post-release bugs. The benefi ts of these uses of the generated invariants are shown through experiments. The high control-flow coverage of generated invariants is demonstrated for the Siemens benchmark suite, and higher quality is indicated when compared with Daikon, a prior technique. Fault injection experiments show high error detection coverage for several types of manifested errors. Results for PREAMBL show higher scoring for localized paths than previous approaches

ENHANCING CLOUD SYSTEM RUNTIME TO ADDRESS COMPLEX FAILURES

As the reliance on cloud systems intensifies in our progressively digital world, understanding and reinforcing their reliability becomes more crucial than ever. Despite impressive advancements in augmenting the resilience of cloud systems, the growing incidence of complex failures now poses a substantial challenge to the availability of these systems. With cloud systems continuing to scale and increase in complexity, failures not only become more elusive to detect but can also lead to more catastrophic consequences. Such failures question the foundational premises of conventional fault-tolerance designs, necessitating the creation of novel system designs to counteract them. This dissertation aims to enhance distributed systems’ capabilities to detect, localize, and react to complex failures at runtime. To this end, this dissertation makes contributions to address three emerging categories of failures in cloud systems. The first part delves into the investigation of partial failures, introducing OmegaGen, a tool adept at generating tailored checkers for detecting and localizing such failures. The second part grapples with silent semantic failures prevalent in cloud systems, showcasing our study findings, and introducing Oathkeeper, a tool that leverages past failures to infer rules and expose these silent issues. The third part explores solutions to slow failures via RESIN, a framework specifically designed to detect, diagnose, and mitigate memory leaks in cloud-scale infrastructures, developed in collaboration with Microsoft Azure. The dissertation concludes by offering insights into future directions for the construction of reliable cloud systems