Persistent Contextual Values as Inter-Process Layers

Mobile applications today often fail to be context aware when they also need to be customizable and efficient at run-time. Context-oriented programming allows programmers to develop applications that are more context aware. Its central construct, the so-called layer, however, is not customizable. We propose to use novel persistent contextual values for mobile development. Persistent contextual values automatically adapt their value to the context. Furthermore they provide access without overhead. Key-value configuration files contain the specification of contextual values and the persisted contextual values themselves. By modifying the configuration files, the contextual values can easily be customized for every context. From the specification, we generate code to simplify development. Our implementation, called Elektra, permits development in several languages including C++ and Java. In a benchmark we compare layer activations between threads and between applications. In a case study involving a web-server on a mobile embedded device the performance overhead is minimal, even with many context switches.Comment: 8 pages Mobile! 16, October 31, 2016, Amsterdam, Netherland

Run-time Variability with Roles

Adaptability is an intrinsic property of software systems that require adaptation to cope with dynamically changing environments. Achieving adaptability is challenging. Variability is a key solution as it enables a software system to change its behavior which corresponds to a specific need. The abstraction of variability is to manage variants, which are dynamic parts to be composed to the base system. Run-time variability realizes these variant compositions dynamically at run time to enable adaptation. Adaptation, relying on variants specified at build time, is called anticipated adaptation, which allows the system behavior to change with respect to a set of predefined execution environments. This implies the inability to solve practical problems in which the execution environment is not completely fixed and often unknown until run time. Enabling unanticipated adaptation, which allows variants to be dynamically added at run time, alleviates this inability, but it holds several implications yielding system instability such as inconsistency and run-time failures. Adaptation should be performed only when a system reaches a consistent state to avoid inconsistency. Inconsistency is an effect of adaptation happening when the system changes the state and behavior while a series of methods is still invoking. A software bug is another source of system instability. It often appears in a variant composition and is brought to the system during adaptation. The problem is even more critical for unanticipated adaptation as the system has no prior knowledge of the new variants. This dissertation aims to achieve anticipated and unanticipated adaptation. In achieving adaptation, the issues of inconsistency and software failures, which may happen as a consequence of run-time adaptation, are evidently addressed as well. Roles encapsulate dynamic behavior used to adapt players representing the base system, which is the rationale to select roles as the software system's variants. Based on the role concept, this dissertation presents three mechanisms to comprehensively address adaptation. First, a dynamic instance binding mechanism is proposed to loosely bind players and roles. Dynamic binding of roles enables anticipated and unanticipated adaptation. Second, an object-level tranquility mechanism is proposed to avoid inconsistency by allowing a player object to adapt only when its consistent state is reached. Last, a rollback recovery mechanism is proposed as a proactive mechanism to embrace and handle failures resulting from a defective composition of variants. A checkpoint of a system configuration is created before adaptation. If a specialized bug sensor detects a failure, the system rolls back to the most recent checkpoint. These mechanisms are integrated into a role-based runtime, called LyRT. LyRT was validated with three case studies to demonstrate the practical feasibility. This validation showed that LyRT is more advanced than the existing variability approaches with respect to adaptation due to its consistency control and failure handling. Besides, several benchmarks were set up to quantify the overhead of LyRT concerning the execution time of adaptation. The results revealed that the overhead introduced to achieve anticipated and unanticipated adaptation to be small enough for practical use in adaptive software systems. Thus, LyRT is suitable for adaptive software systems that frequently require the adaptation of large sets of objects

ContextErlang: A language for distributed context-aware self-adaptive applications

Self-adaptive software modifies its behavior at run time to satisfy changing requirements in a dynamic environment. Context-oriented programming (COP) has been recently proposed as a specialized programming paradigm for context-aware and adaptive systems. COP mostly focuses on run time adaptation of the application’s behavior by supporting modular descriptions of behavioral variations. However, self-adaptive applications must satisfy additional requirements, such as distribution and concurrency, support for unforeseen changes and enforcement of correct behavior in the presence of dynamic change. Addressing these issues at the language level requires a holistic design that covers all aspects and takes into account the possibly cumbersome interaction of those features, for example concurrency and dynamic change. We present ContextErlang, a COP programming language in which adaptive abstractions are seamlessly integrated with distribution and concurrency. We define ContextErlang’s formal semantics, validated through an executable prototype, and we show how it supports formal proofs that the language design ensures satisfaction of certain safety requirements. We provide empirical evidence that ContextErlang is an effective solution through case studies and a performance assessment. We also show how the same design principles that lead to the development of ContextErlang can be followed to systematically design contextual extensions of other languages. A concrete example is presented concerning ContextScala

Run-time Variability with First-class Contexts

Software must be regularly updated to keep up with changing requirements. Unfortunately, to install an update, the system must usually be restarted, which is inconvenient and costly. In this dissertation, we aim at overcoming the need for restart by enabling run-time changes at the programming language level. We argue that the best way to achieve this goal is to improve the support for encapsulation, information hiding and late binding by contextualizing behavior. In our approach, behavioral variations are encapsulated into context objects that alter the behavior of other objects locally. We present three contextual language features that demonstrate our approach. First, we present a feature to evolve software by scoping variations to threads. This way, arbitrary objects can be substituted over time without compromising safety. Second, we present a variant of dynamic proxies that operate by delegation instead of forwarding. The proxies can be used as building blocks to implement contextualization mechanisms from within the language. Third, we contextualize the behavior of objects to intercept exchanges of references between objects. This approach scales information hiding from objects to aggregates. The three language features are supported by formalizations and case studies, showing their soundness and practicality. With these three complementary language features, developers can easily design applications that can accommodate run-time changes

Towards conscious-like behavior in computer game characters

Proceeding of: IEEE Symposium on Computational Intelligence and Games 2009 (CIG-2009). Milano, Italy, 7-10 Septiembre, 2009.The main sources of inspiration for the design of more engaging synthetic characters are existing psychological models of human cognition. Usually, these models, and the associated Artificial Intelligence (AI) techniques, are based on partial aspects of the real complex systems involved in the generation of human-like behavior. Emotions, planning, learning, user modeling, set shifting, and attention mechanisms are some remarkable examples of features typically considered in isolation within classical AI control models. Artificial cognitive architectures aim at integrating many of these aspects together into effective control systems. However, the design of this sort of architectures is not straightforward. In this paper, we argue that current research efforts in the young field of Machine Consciousness (MC) could contribute to tackle complexity and provide a useful framework for the design of more appealing synthetic characters. This hypothesis is illustrated with the application of a novel consciousness-based cognitive architecture to the development of a First Person Shooter video game character.This work was supported by the Spanish Ministry of Education under CICYT grant TRA2007-67374-C02-02.Publicad

CARISMA: a context-sensitive approach to race-condition sample-instance selection for multithreaded applications

Dynamic race detectors can explore multiple thread schedules of a multithreaded program over the same input to detect data races. Although existing sampling-based precise race detectors reduce overheads effectively so that lightweight precise race detection can be performed in testing or post-deployment environments, they are ineffective in detecting races if the sampling rates are low. This paper presents CARISMA to address this problem. CARISMA exploits the insight that along an execution trace, a program may potentially handle many accesses to the memory locations created at the same site for similar purposes. Iterating over multiple execution trials of the same input, CARISMA estimates and distributes the sampling budgets among such location creation sites, and probabilistically collects a fraction of all accesses to the memory locations associated with such sites for subsequent race detection. Our experiment shows that, compared with PACER on the same platform and at the same sampling rate (such as 1%), CARISMA is significantly more effective. © 2012 ACM.postprin

CODEWEAVE: exploring fine-grained mobility of code

This paper is concerned with an abstract exploration of code mobility constructs designed for use in settings where the level of granularity associated with the mobile units exhibits significant variability. Units of mobility that are both finer and coarser grained than the unit of execution are examined. To accomplish this, we take the extreme view that every line of code and every variable declaration are potentially mobile, i.e., it may be duplicated or moved from one program context to another on the same host or across the network. We also assume that complex code assemblies may move with equal ease. The result is CODEWEAVE, a model that shows how to develop new forms of code mobility, assign them precise meaning, and facilitate formal verification of programs employing them. The design of CODEWEAVE relies greatly on Mobile UNITY, a notation and proof logic for mobile computing. Mobile UNITY offers a computational milieu for examining a wide range of constructs and semantic alternatives in a clean abstract setting, i.e., unconstrained by compilation and performance considerations traditionally associated with programming language design. Ultimately, the notation offered by CODEWEAVE is given exact semantic definition by means of a direct mapping to the underlying Mobile UNITY model. The abstract and formal treatment of code mobility offered by CODEWEAVE establishes a technical foundation for examining competing proposals and for subsequent integration of some of the mobility constructs both at the language level and within middleware for mobility

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL

Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N

Forensic Memory Classification using Deep Recurrent Neural Networks

The goal of this project is to advance the application of machine learning frameworks and tools in the process of malware detection. Specifically, a deep neural network architecture is proposed to classify application modules as benign or malicious, using the lower level memory block patterns that make up these modules. The modules correspond to blocks of functionality within files used in kernel and OS level processes as well as user level applications. The learned model is proposed to reside in an isolated core with strict communication restrictions to achieve incorruptibility as well as efficiency, therefore providing a probabilistic memory-level view of the system that is consistent with the user-level view. The lower level memory blocks are constructed using basic block sequences of varying sizes that are fed as input into Long-Short Term Memory models. Four configurations of the LSTM model are explored, by adding bi-directionality as well as Attention. Assembly level data from 50 PE files are extracted and basic blocks are constructed using the IDA Disassembler toolkit. The results show that longer basic block sequences result in richer LSTM hidden layer representations. The hidden states are fed as features into Max pooling layers or Attention layers, depending on the configuration being tested, and the final classification is performed using Logistic Regression with a single hidden layer. The bidirectional LSTM with Attention proved to be the best model, used on basic block sequences of size 29. The differences between the model’s ROC curves indicate a strong reliance on lower level, instructional features, as opposed to metadata or String features, that speak to the success of using entire assembly instructions as data, as opposed to just opcodes or higher level features