Functional testing of faults in asynchronous crossbar architecture

The challenge of extending Moore\u27s Law past the physical limits of the present semiconductor technology calls for novel innovations. Several novel nanotechnologies are being proposed as an alternative to their CMOS counterparts, with nanowire crossbar being one of the most promising paradigms. Quite recently, a new promising clock-free architecture, called the Asynchronous Crossbar Architecture has been proposed to enhance the manufacturability and to improve the robustness of digital circuits by removing various timing related failure modes. Even though the proposed clock-free architecture offers several merits, it is not free from the high defect rates induced due to nondeterministic nanoscale assembly. In this work, a unique Functional Test Algorithm (FTA) has been proposed and validated to test for manufacturing defects in this architecture. The proposed Functional Test Algorithm is aimed at reducing the testing overhead in terms of the time and space complexity associated with the existing sequential test scheme. In addition, it is designed to provide high fault coverage and excellent fault-tolerance via post-reconfiguration. This test scheme can be effectively used to assure true functionality of any threshold gate realized on a given PGMB. The main motivation behind this research is to propose a comprehensive test scheme which can achieve sufficiently high test coverage with acceptable test overhead. This test algorithm is a significant effort towards viable nanoscale computation --Abstract, page iv

Tolerating Correlated Failures in Massively Parallel Stream Processing Engines

Fault-tolerance techniques for stream processing engines can be categorized into passive and active approaches. A typical passive approach periodically checkpoints a processing task's runtime states and can recover a failed task by restoring its runtime state using its latest checkpoint. On the other hand, an active approach usually employs backup nodes to run replicated tasks. Upon failure, the active replica can take over the processing of the failed task with minimal latency. However, both approaches have their own inadequacies in Massively Parallel Stream Processing Engines (MPSPE). The passive approach incurs a long recovery latency especially when a number of correlated nodes fail simultaneously, while the active approach requires extra replication resources. In this paper, we propose a new fault-tolerance framework, which is Passive and Partially Active (PPA). In a PPA scheme, the passive approach is applied to all tasks while only a selected set of tasks will be actively replicated. The number of actively replicated tasks depends on the available resources. If tasks without active replicas fail, tentative outputs will be generated before the completion of the recovery process. We also propose effective and efficient algorithms to optimize a partially active replication plan to maximize the quality of tentative outputs. We implemented PPA on top of Storm, an open-source MPSPE and conducted extensive experiments using both real and synthetic datasets to verify the effectiveness of our approach

Towards Automated Performance Bug Identification in Python

Context: Software performance is a critical non-functional requirement, appearing in many fields such as mission critical applications, financial, and real time systems. In this work we focused on early detection of performance bugs; our software under study was a real time system used in the advertisement/marketing domain. Goal: Find a simple and easy to implement solution, predicting performance bugs. Method: We built several models using four machine learning methods, commonly used for defect prediction: C4.5 Decision Trees, Na\"{\i}ve Bayes, Bayesian Networks, and Logistic Regression. Results: Our empirical results show that a C4.5 model, using lines of code changed, file's age and size as explanatory variables, can be used to predict performance bugs (recall=0.73, accuracy=0.85, and precision=0.96). We show that reducing the number of changes delivered on a commit, can decrease the chance of performance bug injection. Conclusions: We believe that our approach can help practitioners to eliminate performance bugs early in the development cycle. Our results are also of interest to theoreticians, establishing a link between functional bugs and (non-functional) performance bugs, and explicitly showing that attributes used for prediction of functional bugs can be used for prediction of performance bugs

Multi-criteria decision analysis for non-conformance diagnosis: A priority-based strategy combining data and business rules

Business process analytics and verification have become a major challenge for companies, especially when process data is stored across different systems. It is important to ensure Business Process Compliance in both data-flow perspectives and business rules that govern the organisation. In the verification of data-flow accuracy, the conformance of data to business rules is a key element, since essential to fulfil policies and statements that govern corporate behaviour. The inclusion of business rules in an existing and already deployed process, which therefore already counts on stored data, requires the checking of business rules against data to guarantee compliance. If inconsistency is detected then the source of the problem should be determined, by discerning whether it is due to an erroneous rule or to erroneous data. To automate this, a diagnosis methodology following the incorporation of business rules is proposed, which simultaneously combines business rules and data produced during the execution of the company processes. Due to the high number of possible explanations of faults (data and/or business rules), the likelihood of faults has been included to propose an ordered list. In order to reduce these possibilities, we rely on the ranking calculated by means of an AHP (Analytic Hierarchy Process) and incorporate the experience described by users and/or experts. The methodology proposed is based on the Constraint Programming paradigm which is evaluated using a real example. .Ministerio de Ciencia y Tecnología RTI2018–094283-B-C3