A Case Study on Formal Verification of Self-Adaptive Behaviors in a Decentralized System

Self-adaptation is a promising approach to manage the complexity of modern software systems. A self-adaptive system is able to adapt autonomously to internal dynamics and changing conditions in the environment to achieve particular quality goals. Our particular interest is in decentralized self-adaptive systems, in which central control of adaptation is not an option. One important challenge in self-adaptive systems, in particular those with decentralized control of adaptation, is to provide guarantees about the intended runtime qualities. In this paper, we present a case study in which we use model checking to verify behavioral properties of a decentralized self-adaptive system. Concretely, we contribute with a formalized architecture model of a decentralized traffic monitoring system and prove a number of self-adaptation properties for flexibility and robustness. To model the main processes in the system we use timed automata, and for the specification of the required properties we use timed computation tree logic. We use the Uppaal tool to specify the system and verify the flexibility and robustness properties.Comment: In Proceedings FOCLASA 2012, arXiv:1208.432

Automatic Software Repair: a Bibliography

This article presents a survey on automatic software repair. Automatic software repair consists of automatically finding a solution to software bugs without human intervention. This article considers all kinds of repairs. First, it discusses behavioral repair where test suites, contracts, models, and crashing inputs are taken as oracle. Second, it discusses state repair, also known as runtime repair or runtime recovery, with techniques such as checkpoint and restart, reconfiguration, and invariant restoration. The uniqueness of this article is that it spans the research communities that contribute to this body of knowledge: software engineering, dependability, operating systems, programming languages, and security. It provides a novel and structured overview of the diversity of bug oracles and repair operators used in the literature

Software that Learns from its Own Failures

All non-trivial software systems suffer from unanticipated production failures. However, those systems are passive with respect to failures and do not take advantage of them in order to improve their future behavior: they simply wait for them to happen and trigger hard-coded failure recovery strategies. Instead, I propose a new paradigm in which software systems learn from their own failures. By using an advanced monitoring system they have a constant awareness of their own state and health. They are designed in order to automatically explore alternative recovery strategies inferred from past successful and failed executions. Their recovery capabilities are assessed by self-injection of controlled failures; this process produces knowledge in prevision of future unanticipated failures

Vibration-based methods for structural and machinery fault diagnosis based on nonlinear dynamics tools

This study explains and demonstrates the utilisation of different nonlinear-dynamics-based procedures for the purposes of structural health monitoring as well as for monitoring of robot joints

Mining Techniques For Invariants In Cloud Computing

The increasing popularity of Software as a Service (SaaS) stresses the need of solutions to predict failures and avoid service interruptions, which invariably result in SLA violations and severe loss of revenue. A promising approach to continuously monitor the correct functioning of the system is to check the execution conformance to a set of invariants, i.e., properties that must hold when the system is deemed to run correctly. This paper proposes a technique to spot a true anomalies by the use of various data mining techniques like clustering, association rule and decision tree algorithms help in finding the hidden and previously unknown information from the database. We assess the techniques in two invariants’ applications, namely executions characterization and anomaly detection, using the metrics of coverage, recall and precision. In this work two real-world datasets have been used - the publicly available Google datacenter dataset and a dataset of a commercial SaaS utility computing platform - for detecting the anomalies

Directed Security Policies: A Stateful Network Implementation

Large systems are commonly internetworked. A security policy describes the communication relationship between the networked entities. The security policy defines rules, for example that A can connect to B, which results in a directed graph. However, this policy is often implemented in the network, for example by firewalls, such that A can establish a connection to B and all packets belonging to established connections are allowed. This stateful implementation is usually required for the network's functionality, but it introduces the backflow from B to A, which might contradict the security policy. We derive compliance criteria for a policy and its stateful implementation. In particular, we provide a criterion to verify the lack of side effects in linear time. Algorithms to automatically construct a stateful implementation of security policy rules are presented, which narrows the gap between formalization and real-world implementation. The solution scales to large networks, which is confirmed by a large real-world case study. Its correctness is guaranteed by the Isabelle/HOL theorem prover.Comment: In Proceedings ESSS 2014, arXiv:1405.055

Reducing screened program points for efficient error detection

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201