Formal verification problems in a big data world: towards a mighty synergy

Camilli, Matteo

doi:10.1145/2591062.2591088

Cited by 18 publications

(13 citation statements)

References 18 publications

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“…In other words, Big Data helps to improve software engineering processes, and the improved software engineering process can in turn develop better Big Data projects. This approach was not widely seen in the works we surveyedperhaps it is best represented in the paper by Camilli [24]. We will be turning our attention to several approaches to try to spur development of this mutual enrichment idea in the near future.…”

Section: Discussion and Future Researchmentioning

confidence: 99%

“…Given the huge amounts of data generated by BD these systems, new formal verification techniques are required for them [24]. The powerful computing resources associated with cloud computing can fundamentally transform formal verification techniques by parallelizing and distributing the verification techniques themselves.…”

Section: Testing and Debugging Big Data Systemsmentioning

confidence: 99%

“…The powerful computing resources associated with cloud computing can fundamentally transform formal verification techniques by parallelizing and distributing the verification techniques themselves. Camilli [24] introduces a distributed approach to formal verification which exploits Big Data techniques to enable the verification of very large, complex systems using Big Data approaches and cloud computing facilities.…”

Section: Testing and Debugging Big Data Systemsmentioning

confidence: 99%

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Big Data and software engineering: prospects for mutual enrichment

Arndt

2017

Iran J Comput Sci

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Software engineering has evolved over the last 50 years, initially as a response to the so-called software crisis (the problems that organizations had producing quality software systems on time and on budget) of the 1960s and 1970s. Software engineering (SE) has been defined as "the application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software; that is, the application of engineering to software". Software engineering has developed a number of approaches to areas such as software requirements, software design, software testing, and software maintenance. Software development processes such as the waterfall model, incremental development, and the spiral model have been successfully applied to produce high-quality software on time and under budget. More recently, agile software development has gained popularity as an alternative to the more traditional development methods for development of complex systems. Within the last decade or so, advances in technologies such as mobile computing, social networks, cloud computing, and the Internet of things have given rise to massive datasets which have been given the name Big Data (BD). Big Data has been defined as data with 3Vs-high volume, velocity, and variety. Big Data contains so much data that low probability events are captured in the data. These events can be discovered using analytics methods and turned into actionable intelligence which can be used by businesses to gain a competitive advantage. Unfortunately, the very scale of BD often renders inadequate SQL-based relational database systems which have formed the backbone of data intensive systems for the last 30 years, requiring new NoSQL technologies to be effective. In this paper, we will explore how well-established SE technology can be adapted to support successful development of BD projects, as well as how BD techniques can be used to increase the utility of SE processes and techniques. Thus, BD and SE may mutually support and enrich each other.

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Section: Discussion and Future Researchmentioning

confidence: 99%

Section: Testing and Debugging Big Data Systemsmentioning

confidence: 99%

Section: Testing and Debugging Big Data Systemsmentioning

confidence: 99%

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Big Data and software engineering: prospects for mutual enrichment

Arndt

2017

Iran J Comput Sci

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“…Third, from a verification perspective, to the best of our knowledge, this represents the first attempt to build a formal model representing Storm topologies, and the first try in making a configurable model aiming at running verification tasks of non-functional properties for big data applications. While some works concentrate on exploiting big data technologies to speedup verification tasks [15], others focus on the formalization of the specific framework, but remain application-independent, and their goal is rather to verify properties of the framework, such as reliability and load balancing [38], or the validity of the messaging flow in MapReduce [41].…”

Section: Related Workmentioning

confidence: 99%

Verifying big data topologies by-design: a semi-automated approach

et al. 2019

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IntroductionBig data or data-intensive applications (DIAs) process large amounts of data for the purpose of gaining key business intelligence through complex analytics using machinelearning techniques [20,35]. These applications are receiving increased attention in the last years given their ability to yield competitive advantage by direct investigation of user needs and trends hidden in the enormous quantities of data produced daily by the average Internet user. According to Gartner [1] business intelligence and analytics applications will remain a top focus for Chief-Information Officers (CIOs) of most Fortune 500 companies until at least 2019-2021. However, the cost of ownership of the systems that process big data analytics are high due to infrastructure costs, steep learning curves for the different frameworks (such as Apache Storm [21], Apache Spark [2] or Apache Hadoop [3]) typically involved in design and development of big data applications and complexities in large-scale architectures.A key complexity of the above design and development activity lies in quickly and continuously refining the configuration parameters of the middleware and service platforms on top of which the DIA is running [12]. The process in question is especially complex as the number of middleware involved in DIAs design increases; the more middleware are involved the more parameters need co-evaluation (e.g., latency or beaconing times, caching policies, queue retention and more)-fine-tuning these "knobs" on so many Abstract Big data architectures have been gaining momentum in recent years. For instance, Twitter uses stream processing frameworks like Apache Storm to analyse billions of tweets per minute and learn the trending topics. However, architectures that process big data involve many different components interconnected via semantically different connectors. Such complex architectures make possible refactoring of the applications a difficult task for software architects, as applications might be very different with respect to the initial designs. As an aid to designers and developers, we developed OSTIA (Ordinary Static Topology Inference Analysis) that allows detecting the occurrence of common anti-patterns across big data architectures and exploiting software verification techniques on the elicited architectural models. This paper illustrates OSTIA and evaluates its uses and benefits on three industrial-scale case-studies. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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“…Furthermore, aspects such as messaging, communication protocols, which are commonly used in concurrent or distributed systems, can be difficult to model with the language primitives of timed-automata [22,21]. Moreover, TB nets are supported by powerful off-the-shelf open source software tools able to perform simulation, model checking [23,24,25,26], model-based testing, and runtime verification [27,28].…”

Section: Time Basic Petri Netsmentioning

confidence: 99%

Zone-based formal specification and timing analysis of real-time self-adaptive systems

Camilli

Gargantini

Scandurra

2018

Science of Computer Programming

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Self-adaptive software systems are able to autonomously adapt their behavior at run-time to react to internal dynamics and to uncertain and changing environment conditions. Formal specification and verification of self-adaptive systems are tasks generally very difficult to carry out, especially when involving time constraints. In this case, in fact, the system correctness depends also on the time associated with events.This article introduces the Zone-based Time Basic Petri nets specification formalism. The formalism adopts timed adaptation models to specify self-adaptive behavior with temporal constraints, and relies on a zone-based modeling approach to support separation of concerns. Zones identified during the modeling phase can be then used as modules either in isolation, to verify intra-zone properties, or all together, to verify inter-zone properties over the entire system. In addition, the framework allows the verification of (timed) robustness properties to guarantee self-healing capabilities when higher levels of reliability and availability are required to the system, especially when dealing with time-critical systems. This article presents also the ZAFETY tool, a Java software implementation of the proposed framework, and the validation and experimental results obtained in modeling and verifying two time-critical self-adaptive systems: the Gas Burner system and the Unmanned Aerial Vehicle system.

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Formal verification problems in a big data world: towards a mighty synergy

Cited by 18 publications

References 18 publications

Big Data and software engineering: prospects for mutual enrichment

Big Data and software engineering: prospects for mutual enrichment

Verifying big data topologies by-design: a semi-automated approach

Zone-based formal specification and timing analysis of real-time self-adaptive systems

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