Effective Test Algorithm Research Articles

Financially effective test algorithm to identify an aggressive, EGFR-amplified variant of IDH-wildtype, lower-grade diffuse glioma.

Update 3 of the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) recognizes amplification of epidermal growth factor receptor (EGFR) as one important aberration in diffuse gliomas (World Health Organization [WHO] grade II/III). While these recommendations endorse testing, a cost-effective, clinically relevant testing paradigm is currently lacking. Here, we use real-world clinical data to propose a financially effective diagnostic test algorithm in the context of new guidelines. To determine the prevalence, distribution, neuroradiographic features (Visually Accessible REMBRANDT Images [VASARI]), and prognostic relevance of EGFR amplification in lower-grade gliomas, we assembled a consecutive series of diffuse gliomas. For validation we included publicly available data from The Cancer Genome Atlas. For a cost-utility analysis we compared combined EGFR and isocitrate dehydrogenase (IDH) testing, EGFR testing based on IDH results, and no EGFR testing. In n = 71 WHO grade II/III gliomas, we identified EGFR amplification in 28.2%. With one exception, all EGFR amplifications occurred in IDH-wildtype gliomas. Comparison of overall survival showed that EGFR amplification denotes a significantly more aggressive subset of tumors (P < 0.0001, log-rank). The radiologic phenotype in the EGFR-amplified tumors includes diffusion restriction (15%, P = 0.02), >5% tumor contrast enhancement (75%, P = 0.016), and mild (not avid) enhancement (P = 0.016). The proposed testing algorithm reserves EGFR fluorescence in situ hybridization (FISH) testing for IDH-wildtype cases. Implementation would result in ~37.9% cost reduction at our institution, or about $1.3-4 million nationally. EGFR-amplified diffuse gliomas are "glioblastoma-like" in their behavior and may represent undersampled glioblastomas, or subsets of IDH-wildtype diffuse gliomas with inherently aggressive biology. EGFR FISH after IDH testing is a financially effective and clinically relevant test algorithm for routine clinical practice.

On the Functional Test of Branch Prediction Units

Branch prediction units (BPUs) are highly efficient modules that can significantly decrease the negative impact of branches in pipelined processors. Traditional test solutions, mainly based on Design for Testability techniques, are often inadequate to tackle specific test constraints, such as those found when incoming inspection or online test is considered. Following a functional approach based on running a suitable test program and checking the processor behavior may represent an alternative solution, provided that an effective test algorithm is available for the target unit. In this paper, a functional approach targeting the test of the BPU memory is proposed, which leads to the generation of suitable test programs whose effectiveness is independent of the specific implementation of the BPU. Two very common BPU architectures (branch history table and branch target buffer) are considered. The effectiveness of the approach is validated resorting to an open-source computer architectural simulator. Experimental results show that the proposed method is able to thoroughly test the BPU memory, allowing to transform whichever March algorithm into a corresponding test program; we also provide both theoretical and experimental proofs that the memory and execution time requirements grow linearly with the BPU size.

Quadratic Algorithms for Testing of Codes and ◊-Codes

The Sardinas-Patterson's test for codes has contributed many effective testing algorithms to the development of theory of codes, formal languages, etc. However, we will show that a modification of this test proposed in this paper can deduce more effective testing algorithms for codes. As a consequence, we establish a quadratic algorithm that, given as input a regular language X defined by a tuple Φ, M, B, where Φ : A* → M is a monoid morphism saturating X, M is a finite monoid, $B \subseteq M$, X = Φ-1B, decides in time complexity $\mathcal{O}n^2$ whether X is a code, where n = CardM. Specially, n can be chosen as the finite index of X. A quadratic algorithm for testing of ◊-codes is also established.

Generating effective tests for concurrent programs via AI automated planning techniques

Testing concurrent programs is a challenging problem due to interleaving explosion: even for a fixed set of inputs, there is a huge number of concurrent runs that need to be tested to account for scheduler behavior. Testing all possible schedules is not practical. Consequently, most effective testing algorithms only test a select subset of runs. For example, limiting testing to runs that contain data races or atomicity violations has been shown to capture a large proportion of concurrency bugs. In this paper we present a general approach to concurrent program testing that is based on techniques from artificial intelligence (AI) automated planning. We propose a framework for predicting concurrent program runs that violate a collection of generic correctness specifications for concurrent programs, namely runs that contain data races, atomicity violations, or null-pointer dereferences. Our prediction is based on observing an arbitrary run of the program, and using information collected from this run to model the behavior of the program, and to predict new runs that contain bugs with one of the above noted violation patterns. We characterize the problem of predicting such new runs as an AI sequential planning problem with the temporally extended goal of achieving a particular violation pattern. In contrast to many state-of-the-art approaches, in our approach feasibility of the predicted runs is guaranteed and, therefore, all generated runs are fully usable for testing. Moreover, our planning-based approach has the merit that it can easily accommodate a variety of violation patterns which serve as the selection criteria for guiding search in the state space of concurrent runs. This is achieved by simply modifying the planning goal. We have implemented our approach using state-of-the-art AI planning techniques and tested it within the Penelope concurrent program testing framework [35]. Nevertheless, the approach is general and is amenable to a variety of program testing frameworks. Our experiments with a benchmark suite showed that our approach is very fast and highly effective, finding all known bugs.

Verifying cloud service‐level agreement by a third‐party auditor

ABSTRACTIn this paper, we study the important issue of verifying service‐level agreement (SLA) with an untrusted cloud and present an SLA verification framework that utilizes a third‐party auditor (TPA). A cloud provides users with elastic computing and storage resources in a pay‐as‐you‐go way. An SLA between the cloud and a user is a contract that specifies the computing resources and performances that the cloud should provide to the user. A cloud service provider (CSP) has incentives to cheat on the SLA, for example, providing a user with less central processing unit and memory resources than specified in the SLA, which allows the CSP to support more users and make more profits. A malicious CSP can easily disrupt the existing SLA monitoring/verification techniques by interfering with the monitoring/measurement process. A TPA resolves the trust dilemma between a CSP and its users. Under the TPA framework and the untrusted‐cloud threat model, we design two effective testing algorithms that can detect an SLA violation of the virtual machine memory size. Using real experiments, we demonstrate that our algorithms can detect cloud cheating on a virtual machine's memory size (i.e., SLA violations). Furthermore, we show that our testing algorithms can defend various attacks from a malicious CSP, which tries to hide an SLA violation. Copyright © 2013 John Wiley &amp; Sons, Ltd.

AN EFFECTIVE TEST ALGORITHM AND DIAGNOSTIC IMPLEMENTATION FOR EMBEDDED STATIC RANDOM ACCESS MEMORIES

An effective test algorithm and built-in self-test (BIST) with diagnostic support for embedded static random access memories (SRAM) is proposed. This work focuses on implementing the algorithm using the BIST with diagnostic support for a 64 × 8 bit embedded SRAM. The algorithm can locate and identify all the target faults in SRAM. The BIST with diagnostic support is realized by programming using very high speed integrated circuit hardware description language codes and proved very valuable for diagnosing the target faults. When analyzing experimental results, the fault dictionary is constructed from the simulated responses under the given test algorithm and fault models. The fault dictionary shows that the algorithm has a completely diagnostic ratio.

Algebraic aspects of controllability for AR-systems

This paper examines some aspects of controllability for behaviour systems in AR-representation. The approach is based on the theory of dipolynomial modules and dipolynomial matrices. Explicit criteria for controllability in terms of the representing matrices are derived and an effective test algorithm is given. Furthermore, controllability indices are introduced for AR-systems. The new theory is compared with existing index concepts in the literature on linear systems.