Correlating contexts and NFR conflicts from event logs

In the design of autonomous systems, it is important to consider the preferences of the interested parties to improve the user experience. These preferences are often associated with the contexts in which each system is likely to operate. The operational behavior of a system must also meet various non-functional requirements (NFRs), which can present different levels of conflict depending on the operational context. This work aims to model correlations between the individual contexts and the consequent conflicts between NFRs. The proposed approach is based on analyzing the system event logs, tracing them back to the leaf elements at the specification level and providing a contextual explanation of the system’s behavior. The traced contexts and NFR conflicts are then mined to produce Context-Context and Context-NFR conflict sequential rules. The proposed Contextual Explainability (ConE) framework uses BERT-based pre-trained language models and sequential rule mining libraries for deriving the above correlations. Extensive evaluations are performed to compare the existing state-of-the-art approaches. The best-fit solutions are chosen to integrate within the ConE framework. Based on experiments, an accuracy of 80%, a precision of 90%, a recall of 97%, and an F1-score of 88% are recorded for the ConE framework on the sequential rules that were mined

Weakly Z symmetric manifolds

We introduce a new kind of Riemannian manifold that includes weakly-, pseudo- and pseudo projective- Ricci symmetric manifolds. The manifold is defined through a generalization of the so called Z tensor; it is named "weakly Z symmetric" and denoted by (WZS)_n. If the Z tensor is singular we give conditions for the existence of a proper concircular vector. For non singular Z tensor, we study the closedness property of the associated covectors and give sufficient conditions for the existence of a proper concircular vector in the conformally harmonic case, and the general form of the Ricci tensor. For conformally flat (WZS)_n manifolds, we derive the local form of the metric tensor.Comment: 13 page

Extracting goal models from natural language requirement specifications

Unstructured (or, semi-structured) natural language is mostly used to capture the requirement specifications both for legacy software systems and for modern day software systems. The adoption of a formal approach to the specification of the requirements, using goal models, enables rigorous and formal inspections while analyzing the requirements for satisfiability, consistency, completeness, conflicts and ambiguities. However, such a formal approach is often considered burdening for the analysts’ activity as it requires additional skills, and is therefore, discarded a priori. This works aims to bridge the gap between natural language requirement specifications and efficient goal model analysis techniques. We propose a framework that uses extensive natural language processing techniques to transform a set of unstructured natural language requirement specifications to the corresponding goal model. We combine techniques such as parts-of-speech tagging, dependency parsing, contextual and synonymy vector generation with the FiBER transformer model. An extensive unbiased crowd-sourced evaluation of the proposed framework has been performed, showing an acceptability rate (total and partial combined) of 95%. Time and space analyses of our framework also demonstrate the scalability of the proposed solution

Driving the Technology Value Stream by Analyzing App Reviews

An emerging feature of mobile application software is the need to quickly produce new versions to solve problems that emerged in previous versions. This helps adapt to changing user needs and preferences. In a continuous software development process, the user reviews collected by the apps themselves can play a crucial role to detect which components need to be reworked. This paper proposes a novel framework that enables software companies to drive their technology value stream based on the feedback (or reviews) provided by the end-users of an application. The proposed end-to-end framework exploits different Natural Language Processing (NLP) tasks to best understand the needs and goals of the end users. We also provide a thorough and in-depth analysis of the framework, the performance of each of the modules, and the overall contribution in driving the technology value stream. An analysis of reviews with sixteen popular Android Play Store applications from various genres over a long period of time provides encouraging evidence of the effectiveness of the proposed approach

Abstraction and Assume-Guarantee Reasoning for Automated Software Verification

Compositional verification and abstraction are the key techniques to address the state explosion problem associated with model checking of concurrent software. A promising compositional approach is to prove properties of a system by checking properties of its components in an assume-guarantee style. This article proposes a framework for performing abstraction and assume-guarantee reasoning of concurrent C code in an incremental and fully automated fashion. The framework uses predicate abstraction to extract and refine finite state models of software and it uses an automata learning algorithm to incrementally construct assumptions for the compositional verification of the abstract models. The framework can be instantiated with different assume-guarantee rules. We have implemented our approach in the COMFORT reasoning framework and we show how COMFORT out-performs several previous software model checking approaches when checking safety properties of non-trivial concurrent programs

Minimising conflicts among run-time non-functional requirements within DevOps

Significant contributions in the existing literature highlight the potential of softgoal interdependency graphs towards analyzing conflicting non-functional requirements (NFRs). However, such analysis is often at a very abstract level and does not quite consider the run-time performance statistics of NFR operationalizations. On the contrary, some initial empirical evaluations demonstrate the importance of the run-time statistics. In this paper, a framework is proposed that uses these statistics and combines the same with NFR priorities for computing the impact of NFR conflicts. The proposed framework is capable of identifying the best possible set of NFR operationalizations that minimizes the impact of conflicting NFRs. A detailed space analysis of the solution framework helps proving the efficiency of the proposed pruning mechanism in terms of better space management. Furthermore, a Dynamic Bayesian Network (DBN) - based system behavioral model that works on top of the proposed framework, is defined and analyzed. An appropriate tool prototype for the framework is implemented as part of this research

CARET analysis of multithreaded programs

Dynamic Pushdown Networks (DPNs) are a natural model for multithreaded programs with (recursive) procedure calls and thread creation. On the other hand, CARET is a temporal logic that allows to write linear temporal formulas while taking into account the matching between calls and returns. We consider in this paper the model-checking problem of DPNs against CARET formulas. We show that this problem can be effectively solved by a reduction to the emptiness problem of B\"uchi Dynamic Pushdown Systems. We then show that CARET model checking is also decidable for DPNs communicating with locks. Our results can, in particular, be used for the detection of concurrent malware.Comment: Pre-proceedings paper presented at the 27th International Symposium on Logic-Based Program Synthesis and Transformation (LOPSTR 2017), Namur, Belgium, 10-12 October 2017 (arXiv:1708.07854

Inhibition of Orai Channel Function Regulates Mas-Related G Protein-Coupled Receptor-Mediated Responses in Mast Cells

Mast cells (MCs) are tissue resident immune cells that play important roles in the pathogenesis of allergic disorders. These responses are mediated via the cross-linking of cell surface high affinity IgE receptor (FcϵRI) by antigen resulting in calcium (Ca2+) mobilization, followed by degranulation and release of proinflammatory mediators. In addition to FcϵRI, cutaneous MCs express Mas-related G protein-coupled receptor X2 (MRGPRX2; mouse ortholog MrgprB2). Activation of MRGPRX2/B2 by the neuropeptide substance P (SP) is implicated in neurogenic inflammation, chronic urticaria, mastocytosis and atopic dermatitis. Although Ca2+ entry is required for MRGPRX2/B2-mediated MC responses, the possibility that calcium release-activated calcium (CRAC/Orai) channels participate in these responses has not been tested. Lentiviral shRNA-mediated silencing of Orai1, Orai2 or Orai3 in a human MC line (LAD2 cells) resulted in partial inhibition of SP-induced Ca2+ mobilization, degranulation and cytokine/chemokine generation (TNF-α, IL-8, and CCL-3). Synta66, which blocks homo and hetero-dimerization of Orai channels, caused a more robust inhibition of SP-induced responses than knockdown of individual Orai channels. Synta66 also blocked SP-induced extracellular signal-regulated kinase 1/2 (ERK1/2) and Akt phosphorylation and abrogated cytokine/chemokine production. It also inhibited SP-induced Ca2+ mobilization and degranulation in primary human skin MCs and mouse peritoneal MCs. Furthermore, Synta66 attenuated both SP-induced cutaneous vascular permeability and leukocyte recruitment in mouse peritoneum. These findings demonstrate that Orai channels contribute to MRGPRX2/B2-mediated MC activation and suggest that their inhibition could provide a novel approach for the modulation of SP-induced MC/MRGPRX2-mediated disorders. Copyright © 2022 Chaki, Alkanfari, Roy, Amponnawarat, Hui, Oskeritzian and Ali

Entomological Surveillance as a Cornerstone of Malaria Elimination: A Critical Appraisal

Global capacity for developing new insecticides and vector control products, as well as mathematical models to evaluate their likely impact upon malaria transmission has greatly improved in recent years. Given that a range of new vector control products are now emerging that target a greater diversity of adult mosquito behaviours, it should soon be feasible to effectively tackle a broader range of mosquito species and settings. However, the primary obstacles to further progress towards more effective malaria vector control are now paucities of routine programmatic entomological surveillance, and capacity for data processing, analysis and interpretation in endemic countries. Well-established entomological methods need to be more widely utilized for routine programmatic surveillance of vector behaviours and insecticide susceptibility, the effectiveness of vector control products and processes, and their impacts on mosquito populations. Such programmatic data may also be useful for simulation analyses of mosquito life histories, to identify opportunities for pre-emptively intervening early in the life cycle of mosquitoes, rather than targeting transmission events occurring when they are older. Current obstacles to more effective utilization, archiving and sharing of entomological data largely centre around global inequities of analytical capacity. These prohibitive and unfair imbalances can be addressed by reorienting funding schemes to emphasize south-centred collaborations focused on malaria-endemic countries