33,724 research outputs found
Model-based dependability analysis : state-of-the-art, challenges and future outlook
Abstract: Over the past two decades, the study of model-based dependability analysis has gathered significant research interest. Different approaches have been developed to automate and address various limitations of classical dependability techniques to contend with the increasing complexity and challenges of modern safety-critical system. Two leading paradigms have emerged, one which constructs predictive system failure models from component failure models compositionally using the topology of the system. The other utilizes design models - typically state automata - to explore system behaviour through fault injection. This paper reviews a number of prominent techniques under these two paradigms, and provides an insight into their working mechanism, applicability, strengths and challenges, as well as recent developments within these fields. We also discuss the emerging trends on integrated approaches and advanced analysis capabilities. Lastly, we outline the future outlook for model-based dependability analysis
Automatic March tests generation for static and dynamic faults in SRAMs
New memory production modern technologies introduce new classes of faults usually referred to as dynamic memory faults. Although some hand-made March tests to deal with these new faults have been published, the problem of automatically generate March tests for dynamic faults has still to be addressed, in this paper we propose a new approach to automatically generate March tests with minimal length for both static and dynamic faults. The proposed approach resorts to a formal model to represent faulty behaviors in a memory and to simplify the generation of the corresponding tests
Compositional synthesis of temporal fault trees from state machines
Dependability analysis of a dynamic system which is embedded with several complex interrelated components raises two main problems. First, it is difficult to represent in a single coherent and complete picture how the system and its constituent parts behave in conditions of failure. Second, the analysis can be unmanageable due to a considerable number of failure events, which increases with the number of components involved. To remedy this problem, in this paper we outline an analysis approach that converts failure behavioural models (state machines) to temporal fault trees (TFTs), which can then be analysed using Pandora -- a recent technique for introducing temporal logic to fault trees. The approach is compositional and potentially more scalable, as it relies on the synthesis of large system TFTs from smaller component TFTs. We show, by using a Generic Triple Redundant (GTR) system, how the approach enables a more accurate and full analysis of an increasingly complex system
IntRepair: Informed Repairing of Integer Overflows
Integer overflows have threatened software applications for decades. Thus, in
this paper, we propose a novel technique to provide automatic repairs of
integer overflows in C source code. Our technique, based on static symbolic
execution, fuses detection, repair generation and validation. This technique is
implemented in a prototype named IntRepair. We applied IntRepair to 2,052C
programs (approx. 1 million lines of code) contained in SAMATE's Juliet test
suite and 50 synthesized programs that range up to 20KLOC. Our experimental
results show that IntRepair is able to effectively detect integer overflows and
successfully repair them, while only increasing the source code (LOC) and
binary (Kb) size by around 1%, respectively. Further, we present the results of
a user study with 30 participants which shows that IntRepair repairs are more
than 10x efficient as compared to manually generated code repairsComment: Accepted for publication at the IEEE TSE journal. arXiv admin note:
text overlap with arXiv:1710.0372
A synthesis of logic and bio-inspired techniques in the design of dependable systems
Much of the development of model-based design and dependability analysis in the design of dependable systems, including software intensive systems, can be attributed to the application of advances in formal logic and its application to fault forecasting and verification of systems. In parallel, work on bio-inspired technologies has shown potential for the evolutionary design of engineering systems via automated exploration of potentially large design spaces. We have not yet seen the emergence of a design paradigm that effectively combines these two techniques, schematically founded on the two pillars of formal logic and biology, from the early stages of, and throughout, the design lifecycle. Such a design paradigm would apply these techniques synergistically and systematically to enable optimal refinement of new designs which can be driven effectively by dependability requirements. The paper sketches such a model-centric paradigm for the design of dependable systems, presented in the scope of the HiP-HOPS tool and technique, that brings these technologies together to realise their combined potential benefits. The paper begins by identifying current challenges in model-based safety assessment and then overviews the use of meta-heuristics at various stages of the design lifecycle covering topics that span from allocation of dependability requirements, through dependability analysis, to multi-objective optimisation of system architectures and maintenance schedules
A synthesis of logic and biology in the design of dependable systems
The technologies of model-based design and dependability analysis in the design of dependable systems, including software intensive systems, have advanced in recent years. Much of this development can be attributed to the application of advances in formal logic and its application to fault forecasting and verification of systems. In parallel, work on bio-inspired technologies has shown potential for the evolutionary design of engineering systems via automated exploration of potentially large design spaces. We have not yet seen the emergence of a design paradigm that combines effectively and throughout the design lifecycle these two techniques which are schematically founded on the two pillars of formal logic and biology. Such a design paradigm would apply these techniques synergistically and systematically from the early stages of design to enable optimal refinement of new designs which can be driven effectively by dependability requirements. The paper sketches such a model-centric paradigm for the design of dependable systems that brings these technologies together to realise their combined potential benefits
FixMiner: Mining Relevant Fix Patterns for Automated Program Repair
Patching is a common activity in software development. It is generally
performed on a source code base to address bugs or add new functionalities. In
this context, given the recurrence of bugs across projects, the associated
similar patches can be leveraged to extract generic fix actions. While the
literature includes various approaches leveraging similarity among patches to
guide program repair, these approaches often do not yield fix patterns that are
tractable and reusable as actionable input to APR systems. In this paper, we
propose a systematic and automated approach to mining relevant and actionable
fix patterns based on an iterative clustering strategy applied to atomic
changes within patches. The goal of FixMiner is thus to infer separate and
reusable fix patterns that can be leveraged in other patch generation systems.
Our technique, FixMiner, leverages Rich Edit Script which is a specialized tree
structure of the edit scripts that captures the AST-level context of the code
changes. FixMiner uses different tree representations of Rich Edit Scripts for
each round of clustering to identify similar changes. These are abstract syntax
trees, edit actions trees, and code context trees. We have evaluated FixMiner
on thousands of software patches collected from open source projects.
Preliminary results show that we are able to mine accurate patterns,
efficiently exploiting change information in Rich Edit Scripts. We further
integrated the mined patterns to an automated program repair prototype,
PARFixMiner, with which we are able to correctly fix 26 bugs of the Defects4J
benchmark. Beyond this quantitative performance, we show that the mined fix
patterns are sufficiently relevant to produce patches with a high probability
of correctness: 81% of PARFixMiner's generated plausible patches are correct.Comment: 31 pages, 11 figure
Learning Tractable Probabilistic Models for Fault Localization
In recent years, several probabilistic techniques have been applied to
various debugging problems. However, most existing probabilistic debugging
systems use relatively simple statistical models, and fail to generalize across
multiple programs. In this work, we propose Tractable Fault Localization Models
(TFLMs) that can be learned from data, and probabilistically infer the location
of the bug. While most previous statistical debugging methods generalize over
many executions of a single program, TFLMs are trained on a corpus of
previously seen buggy programs, and learn to identify recurring patterns of
bugs. Widely-used fault localization techniques such as TARANTULA evaluate the
suspiciousness of each line in isolation; in contrast, a TFLM defines a joint
probability distribution over buggy indicator variables for each line. Joint
distributions with rich dependency structure are often computationally
intractable; TFLMs avoid this by exploiting recent developments in tractable
probabilistic models (specifically, Relational SPNs). Further, TFLMs can
incorporate additional sources of information, including coverage-based
features such as TARANTULA. We evaluate the fault localization performance of
TFLMs that include TARANTULA scores as features in the probabilistic model. Our
study shows that the learned TFLMs isolate bugs more effectively than previous
statistical methods or using TARANTULA directly.Comment: Fifth International Workshop on Statistical Relational AI (StaR-AI
2015
Automatic Generation of Minimal Cut Sets
A cut set is a collection of component failure modes that could lead to a
system failure. Cut Set Analysis (CSA) is applied to critical systems to
identify and rank system vulnerabilities at design time. Model checking tools
have been used to automate the generation of minimal cut sets but are generally
based on checking reachability of system failure states. This paper describes a
new approach to CSA using a Linear Temporal Logic (LTL) model checker called BT
Analyser that supports the generation of multiple counterexamples. The approach
enables a broader class of system failures to be analysed, by generalising from
failure state formulae to failure behaviours expressed in LTL. The traditional
approach to CSA using model checking requires the model or system failure to be
modified, usually by hand, to eliminate already-discovered cut sets, and the
model checker to be rerun, at each step. By contrast, the new approach works
incrementally and fully automatically, thereby removing the tedious and
error-prone manual process and resulting in significantly reduced computation
time. This in turn enables larger models to be checked. Two different
strategies for using BT Analyser for CSA are presented. There is generally no
single best strategy for model checking: their relative efficiency depends on
the model and property being analysed. Comparative results are given for the
A320 hydraulics case study in the Behavior Tree modelling language.Comment: In Proceedings ESSS 2015, arXiv:1506.0325
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