120,794 research outputs found
Letter from Sergeant Major T. King, France, to Violet Wise, Overpeck, Ohio, March 25, 1945
No full textSubgroup separability, knot groups, and graph manifolds
No full textThis paper answers a question of Burns, Karrass and Solitar by giving examples of knot and link groups which are not subgroup-separable. For instance, it is shown that the fundamental group of the square knot complement is not subgroup separable. We characterise the Graph Manifolds with subgroup separable fundamental group as precisely the geometric ones, i.e. the Seifert Fibered 3-manifolds and the Sol manifolds, and show that there is a specific non-subgroup separable group which is a subgroup in all other cases
Wise, T W, 418033
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/426788Surname: WISE. Given Name(s) or Initials: T W. Military Service Number or Last Known Location: 418033. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 50687.248648
Item: [2016.0049.59049] "Wise, T W, 418033
t-Wise Berge and t-Heavy Hypergraphs
No full textIn many proofs concerning extremal parameters of Berge hypergraphs one starts with analyzing that part of that shadow graph which is contained in many hyperedges. Capturing this phenomenon we introduce two new types of hypergraphs. A hypergraph is a -heavy copy of a graph if there is a copy of on its vertex set such that each edge of is contained in at least hyperedges of . is a -wise Berge copy of if additionally for distinct edges of those hyperedges are distinct. We extend known upper bounds on the Turán number of Berge hypergraphs to the -wise Berge hypergraphs case. We asymptotically determine the Turán number of -heavy and -wise Berge copies of long paths and cycles and exactly determine the Turán number of -heavy and -wise Berge copies of cliques. In the case of 3-uniform hypergraphs, we consider the problem in more details and obtain additional results
MulTi-Wise Sampling: Trading Uniform T-Wise Feature Interaction Coverage for Smaller Samples
No full textEnsuring the functional safety of highly configurable systems often requires testing representative subsets of all possible configurations to reduce testing effort and save resources. The ratio of covered t-wise feature interactions (i.e., T-Wise Feature Interaction Coverage) is a common criterion for determining whether a subset of configurations is representative and capable of finding faults. Existing t-wise sampling algorithms uniformly cover t-wise feature interactions for all features, resulting in lengthy execution times and large sample sizes, particularly when large t-wise feature interactions are considered (i.e., high values of t). In this paper, we introduce a novel approach to t-wise feature interaction sampling, questioning the necessity of uniform coverage across all t-wise feature interactions, called \emph{\mulTiWise{}}. Our approach prioritizes between subsets of critical and non-critical features, considering higher t-values for subsets of critical features when generating a t-wise feature interaction sample. We evaluate our approach using subject systems from real-world applications, including \busybox{}, \soletta{}, \fiasco{}, and \uclibc{}. Our results show that sacrificing uniform t-wise feature interaction coverage between all features reduces the time needed to generate a sample and the resulting sample size. Hence, \mulTiWise{} Sampling offers an alternative to existing approaches if knowledge about feature criticality is available
A Scalable t-wise Coverage Estimator
No full textOwing to the pervasiveness of software in our modern lives, software systems have evolved to be highly configurable. Combinatorial testing has emerged as a dominant paradigm for testing highly configurable systems. Often constraints are employed to define the environments where a given system under test (SUT) is expected to work. Therefore, there has been a sustained interest in designing constraint-based test suite generation techniques. A significant goal of test suite generation techniques is to achieve t-wise coverage for higher values of t. Therefore, designing scalable techniques that can estimate t-wise coverage for a given set of tests and/or the estimation of maximum achievable t-wise coverage under a given set of constraints is of crucial importance. The existing estimation techniques face significant scalability hurdles.
The primary scientific contribution of this work is the design of scalable algorithms with mathematical guarantees to estimate (i) t-wise coverage for a given set of tests, and (ii) maximum t-wise coverage for a given set of constraints. In particular, we design a scalable framework ApproxCov that takes in a test set u, a coverage parameter t, a tolerance parameter ε, and a confidence parameter δ, and returns an estimate of the t-wise coverage of u that is guaranteed to be within (1 ± ε)-factor of the ground truth with probability at least 1 - δ. We design a scalable framework ApproxMaxCov that, for a given formula F, a coverage parameter t, a tolerance parameter ε, and a confidence parameter δ, outputs an approximation which is guaranteed to be within (1 ± ε) factor of the maximum achievable t-wise coverage under F, with probability ≥ 1 - δ. Our comprehensive evaluation demonstrates that ApproxCov and ApproxMaxCov can handle benchmarks that are beyond the reach of current state-of-the-art approaches. We believe that the availability of ApproxCov and ApproxMaxCov will enable test suite designers to evaluate the effectiveness of their generators and thereby significantly impact the development of combinatorial testing techniques
T-Wise Presence Condition Coverage and Sampling for Configurable Systems
No full textSampling techniques, such as t-wise interaction sampling are used to enable efficient testing for configurable systems. This is achieved by generating a small yet representative sample of configurations for a system, which circumvents testing the entire solution space. However, by design, most recent approaches for t-wise interaction sampling only consider combinations of configuration options from a configurable system’s variability model and do not take into account their mapping onto the solution space, thus potentially leaving critical implementation artifacts untested. Tartler et al. address this problem by considering presence conditions of implementation artifacts rather than pure configuration options, but do not consider the possible interactions between these artifacts. In this paper, we introduce
t-wise presence condition coverage, which extends the approach of Tartler et al. by using presence conditions extracted from the code as basis to cover t-wise interactions. This ensures that all t-wise interactions of implementation artifacts are included in the sample and that the chance of detecting combinations of faulty configuration options is increased. We evaluate our approach in terms of testing efficiency and testing effectiveness by comparing the approach to existing t-wise interaction sampling techniques. We show that t-wise presence condition sampling is able to produce mostly smaller samples compared to t-wise interaction sampling, while guaranteeing a t-wise presence condition coverage of 100%
A recursive construction of t-wise uniform permutations
No full textWe present a recursive construction of a (2t + 1)-wise uniform set of permutations on 2n objects using a (2t+ 1) − (2n, n, ·) combinatorial design, a t-wise uniform set of permutations on n objects and a (2t+1)-wise uniform set of permutations on n objects. Using the complete design in this procedure gives a t-wise uniform set of permutations on n objects whose size is at most t2n, the first non-trivial construction of an infinite family of t-wise uniform sets for t ≥ 4. If a non-trivial design with suitable parameters is found, it will imply a corresponding improvement in the construction
Evaluering av t-wise testning av REST APIer
No full textA combinatorial explosion can occur when all possible combinations of all input parameters of a system are tested. When the number of input parameters and their possible values increase, the number of tests needed to cover each new case increases exponentially. Combinatorial interaction testing (CIT) is a black-box testing technique used to avoid a combinatorial explosion. CIT finds errors that are triggered by the interactions between parameters. One of the so-called combination strategies that can be used for CIT is t-wise testing. T-wise testing requires at least one test case for each combination of any t parameter values where t is the chosen strength - the number of parameters amongst which the interactions are tested. In this report, CIT with t-wise testing is applied to the testing of REST APIs. The thesis examines how 1-wise, 2-wise and 3-wise interaction testing compare when it comes to detecting injected faults in the code of REST APIs. It also studies the effectiveness of the three t-wise combinations in terms of run-time. The questions were examined through three API endpoints where the 1-wise, 2-wise and 3-wise test suites were generated directly from their OpenAPI specification. These test suites were constructed with parameter values in accordance with boundary value analysis and equivalence class partitioning. To answer the questions of this study, mutation testing was used as a means of injecting faults into the system under test. The mutants were developed specifically for this study as the input validation of the affected APIs was done through code annotations of which there are not many established mutation operators. The results of this study show that 1-wise, 2-wise and 3-wise testing of REST APIs all detect the same injected faults when performing mutation testing on the test suites. Furthermore, it showed that the run-time of the test suites increases significantly as the strength of t-wise testing increases. However, these results are limited to this particular study and cannot be assumed to coincide with other REST APIs. To further strengthen this conclusion for the general case, possible future work is suggested.En kombinatorisk explosion kan uppstå när all möjliga kombinationer av inputparametrar av ett system testas. Antal testfall som behövs för att täcka alla kombinationer av inputparametrar växer exponentiellt när antalet parametrar och dess möjliga värden ökar. För att undvika en kombinatorisk explosion används kombinatorisk interaktionstestning vilket är en typ av black box testning. Syftet med kombinatorisk interaktionstestning är att hitta de fel som uppstår på grund av interaktioner mellan parametrar. Kombinatorisk interaktionstestning har ett flertal så kallade kombinationsstrategier och den kombinationsstrategi som denna rapport använder är t-wise testning. Kravet med t-wise testning är att skapa åtminstone ett testfall för varje kombination av t parametervärden där t är styrkan mätt i antalet parametrar som interaktioner sinsemellan testas. Denna rapport evaluerar effekterna av t-wise testning som en testningsmetod för REST APIer. Uppsatsen jämför hur väl 1-wise, 2-wise och 3-wise interaktionstestning upptäcker injicerade fel i källkoden av ett REST API. Utöver det undersöks även hur effektiva testsekvenserna är med hänsyn till exekveringstiden. Dessa aspekter undersöktes med hjälp av tre API slutpunkter där testsekvenser för 1-wise, 2-wise och 3-wise skapades direkt utifrån deras OpenAPI specifikationer. Testsekvenskerna som skapades använde gränsvärdeanalys och ekvivalensklasspartitionering för att generera parametervärden. För att kunna besvara frågorna i uppsatsen användes mutationstestning för att injicera fel i källkoden. I det här fallet utvecklades mutanterna specifikt för studien eftersom APIernas inputvalidering bestod av valideringsregler vilka inte har fastställda mutationsoperatorer. Resultaten av studien visade att både 1-wise, 2-wise och 3-wise testning av REST APIer kunde identifiera samma injicerade fel. Därutöver förlängs exekveringstiden av en testsekvens markant när styrkan av t-wise testning ökar. Däremot är dessa resultat begränsade till just detta arbete och kan inte antas stämma för alla fall at t-wise testning av REST APIer. För att stärka slutsatsen av detta arbete och generalisera svaren på frågorna föreslås framtida arbeten som kan göras
A Scalable t-wise Coverage Estimator: Algorithms and Applications
No full textOwing to the pervasiveness of software in our modern lives, software systems have evolved to be highly configurable. Combinatorial testing has emerged as a dominant paradigm for testing highly configurable systems. Often constraints are employed to define the environments where a given system is expected to work. Therefore,there has been a sustained interest in designing constraint-based test suite generation techniques. A significant goal of test suite generation techniques is to achieve t-wise coverage for higher values of t. Therefore, designing scalable techniques that can estimate t-wise coverage for a given set of tests and/or the estimation of maximum achievable t-wise coverage under a given set of constraints is of crucial importance. The existing estimation techniques face significant scalability hurdles. We designed scalable algorithms with mathematical guarantees to estimate (i) t-wise coverage for a given set of tests, and (ii) maximum t-wise coverage for a given set of constraints. In particular, ApproxCov takes in a test set U and returns an estimate of the t-wise coverage of U that is guaranteed to be within (1 ± ε)-factor of the ground truth with probability at least 1 − δ for a given tolerance parameter ε and a confidence parameter δ . A scalable framework ApproxMaxCov for a given formula F outputs an approximation which is guaranteed to be within (1 ± ε) factor of the maximum achievable t-wise coverage under F, with probability ≥ 1 − δ for a given tolerance parameter ε and a confidence parameter δ . Our comprehensive evaluation demonstrates that ApproxCov and ApproxMaxCov can handle benchmarks that are beyond the reach of current state-of-the-art approaches. In this paper we present proofs of correctness of ApproxCov, ApproxMaxCov, and of their generalizations. We show how the algorithms can improve the scalability of a test suite generator while maintaining its effectiveness. In addition, we compare several test suite generators on different feature combination sizes t
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