2,567 research outputs found
ConSUS: A light-weight program conditioner
Program conditioning consists of identifying and removing a set of statements which cannot be executed when a condition of interest holds at some point in a program. It has been applied to problems in maintenance, testing, re-use and re-engineering. All current approaches to program conditioning rely upon both symbolic execution and reasoning about symbolic predicates. The reasoning can be performed by a âheavy dutyâ theorem prover but this may impose unrealistic performance constraints.
This paper reports on a lightweight approach to theorem proving using the FermaT Simplify decision procedure. This is used as a component to ConSUS, a program conditioning system for the Wide Spectrum Language WSL. The paper describes the symbolic execution algorithm used by ConSUS, which prunes as it conditions.
The paper also provides empirical evidence that conditioning produces a significant reduction in program size and, although exponential in the worst case, the conditioning system has low degree polynomial behaviour in many cases, thereby making it scalable to unit level applications of program conditioning
Structural testing techniques for the selective revalidation of software
The research in this thesis addresses the subject of regression testing. Emphasis is placed on developing a technique for selective revalidation which can be used during software maintenance to analyse and retest only those parts of the program affected by changes. In response to proposed program modifications, the technique assists the maintenance programmer in assessing the extent of the program alterations, in selecting a representative set of test cases to rerun, and in identifying any test cases in the test suite which are no longer required because of the program changes. The proposed technique involves the application of code analysis techniques and operations research. Code analysis techniques are described which derive information about the structure of a program and are used to determine the impact of any modifications on the existing program code. Methods adopted from operations research are then used to select an optimal set of regression tests and to identify any redundant test cases. These methods enable software, which has been validated using a variety of structural testing techniques, to be retested. The development of a prototype tool suite, which can be used to realise the technique for selective revalidation, is described. In particular, the interface between the prototype and existing regression testing tools is discussed. Moreover, the effectiveness of the technique is demonstrated by means of a case study and the results are compared with traditional regression testing strategies and other selective revalidation techniques described in this thesis
Segment Routing: a Comprehensive Survey of Research Activities, Standardization Efforts and Implementation Results
Fixed and mobile telecom operators, enterprise network operators and cloud
providers strive to face the challenging demands coming from the evolution of
IP networks (e.g. huge bandwidth requirements, integration of billions of
devices and millions of services in the cloud). Proposed in the early 2010s,
Segment Routing (SR) architecture helps face these challenging demands, and it
is currently being adopted and deployed. SR architecture is based on the
concept of source routing and has interesting scalability properties, as it
dramatically reduces the amount of state information to be configured in the
core nodes to support complex services. SR architecture was first implemented
with the MPLS dataplane and then, quite recently, with the IPv6 dataplane
(SRv6). IPv6 SR architecture (SRv6) has been extended from the simple steering
of packets across nodes to a general network programming approach, making it
very suitable for use cases such as Service Function Chaining and Network
Function Virtualization. In this paper we present a tutorial and a
comprehensive survey on SR technology, analyzing standardization efforts,
patents, research activities and implementation results. We start with an
introduction on the motivations for Segment Routing and an overview of its
evolution and standardization. Then, we provide a tutorial on Segment Routing
technology, with a focus on the novel SRv6 solution. We discuss the
standardization efforts and the patents providing details on the most important
documents and mentioning other ongoing activities. We then thoroughly analyze
research activities according to a taxonomy. We have identified 8 main
categories during our analysis of the current state of play: Monitoring,
Traffic Engineering, Failure Recovery, Centrally Controlled Architectures, Path
Encoding, Network Programming, Performance Evaluation and Miscellaneous...Comment: SUBMITTED TO IEEE COMMUNICATIONS SURVEYS & TUTORIAL
Automated Software Transplantation
Automated program repair has excited researchers for more than a decade, yet it has yet to find full scale deployment in industry. We report our experience with SAPFIX: the first deployment of automated end-to-end fault fixing, from test case design through to deployed repairs in production code. We have used SAPFIX at Facebook to repair 6 production systems, each consisting of tens of millions of lines of code, and which are collectively used by hundreds of millions of people worldwide. In its first three months of operation, SAPFIX produced 55 repair candidates for 57 crashes reported to SAPFIX, of which 27 have been deem as correct by developers and 14 have been landed into production automatically by SAPFIX. SAPFIX has thus demonstrated the potential of the search-based repair research agenda by deploying, to hundreds of millions of users worldwide, software systems that have been automatically tested and repaired. Automated software transplantation (autotransplantation) is a form of automated software engineering, where we use search based software engineering to be able to automatically move a functionality of interest from a âdonorâ program that implements it into a âhostâ program that lacks it. Autotransplantation is a kind of automated program repair where we repair the âhostâ program by augmenting it with the missing functionality. Automated software transplantation would open many exciting avenues for software development: suppose we could autotransplant code from one system into another, entirely unrelated, system, potentially written in a different programming language. Being able to do so might greatly enhance the software engineering practice, while reducing the costs. Automated software transplantation manifests in two different flavors: monolingual, when the languages of the host and donor programs is the same, or multilingual when the languages differ. This thesis introduces a theory of automated software transplantation, and two algorithms implemented in two tools that achieve this: ”SCALPEL for monolingual software transplantation and ÏSCALPEL for multilingual software transplantation. Leveraging lightweight annotation, program analysis identifies an organ (interesting behavior to transplant); testing validates that the organ exhibits the desired behavior during its extraction and after its implantation into a host. We report encouraging results: in 14 of 17 monolingual transplantation experiments involving 6 donors and 4 hosts, popular real-world systems, we successfully autotransplanted 6 new functionalities; and in 10 out of 10 multlingual transplantation experiments involving 10 donors and 10 hosts, popular real-world systems written in 4 different programming languages, we successfully autotransplanted 10 new functionalities. That is, we have passed all the test suites that validates the new functionalities behaviour and the fact that the initial program behaviour is preserved. Additionally, we have manually checked the behaviour exercised by the organ. Autotransplantation is also very useful: in just 26 hours computation time we successfully autotransplanted the H.264 video encoding functionality from the x264 system to the VLC media player, a task that is currently done manually by the developers of VLC, since 12 years ago. We autotransplanted call graph generation and indentation for C programs into Kate, (a popular KDE based test editor used as an IDE by a lot of C developers) two features currently missing from Kate, but requested by the users of Kate. Autotransplantation is also efficient: the total runtime across 15 monolingual transplants is 5 hours and a half; the total runtime across 10 multilingual transplants is 33 hours
Machine Learning-based Orchestration Solutions for Future Slicing-Enabled Mobile Networks
The fifth generation mobile networks (5G) will incorporate novel technologies such as network programmability and virtualization enabled by Software-Defined Networking (SDN) and Network Function Virtualization (NFV) paradigms, which have recently attracted major
interest from both academic and industrial stakeholders.
Building on these concepts, Network Slicing raised as the main driver of a novel business model where mobile operators may open, i.e., âsliceâ, their infrastructure to new business players and offer independent, isolated and self-contained sets of network functions
and physical/virtual resources tailored to specific services requirements. While Network Slicing has the potential to increase the revenue sources of service providers, it involves a number of technical challenges that must be carefully addressed.
End-to-end (E2E) network slices encompass time and spectrum resources in the radio access network (RAN), transport resources on the fronthauling/backhauling links, and computing and storage resources at core and edge data centers. Additionally, the vertical service requirementsâ heterogeneity (e.g., high throughput, low latency, high reliability) exacerbates the need for novel orchestration solutions able to manage end-to-end network slice resources across different domains, while satisfying stringent service level agreements and specific traffic requirements. An end-to-end network slicing orchestration solution shall i) admit network slice requests
such that the overall system revenues are maximized, ii) provide the required resources across different network domains to fulfill the Service Level Agreements (SLAs) iii) dynamically adapt the resource allocation based on the real-time traffic load, endusersâ mobility and instantaneous wireless channel statistics. Certainly, a mobile network represents a fast-changing scenario characterized by complex
spatio-temporal relationship connecting end-usersâ traffic demand with social activities and economy. Legacy models that aim at providing dynamic resource allocation based on traditional traffic demand forecasting techniques fail to capture these important aspects.
To close this gap, machine learning-aided solutions are quickly arising as promising technologies to sustain, in a scalable manner, the set of operations required by the network slicing context. How to implement such resource allocation schemes among slices, while
trying to make the most efficient use of the networking resources composing the mobile infrastructure, are key problems underlying the network slicing paradigm, which will be addressed in this thesis
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