Execution History Research Articles

Plume: Efficient and Complete Black-Box Checking of Weak Isolation Levels

Modern databases embrace weak isolation levels to cater for highly available transactions. However, weak isolation bugs have recently manifested in many production databases. This raises the concern of whether database implementations actually deliver their promised isolation guarantees in practice. In this paper we present Plume, the first efficient, complete, black-box checker for weak isolation levels. Plume builds on modular, fine-grained, transactional anomalous patterns, with which we establish sound and complete characterizations of representative weak isolation levels, including read committed, read atomicity, and transactional causal consistency. Plume leverages a novel combination of two techniques, vectors and tree clocks, to accelerate isolation checking. Our extensive assessment shows that Plume can reproduce all known violations in a large collection of anomalous database execution histories, detect new isolation bugs in three production databases along with informative counterexamples, find more weak isolation anomalies than the state-of-the-art checkers, and efficiently validate isolation guarantees under a wide variety of workloads.

Strong Simple Policies for POMDPs

The synthesis problem for partially observable Markov decision processes (POMDPs) is to compute a policy that provably adheres to one or more specifications. Yet, the general problem is undecidable, and policies require full (and thus potentially unbounded) traces of execution history. To provide good approximations of such policies, POMDP agents often employ randomization over action choices. We consider the problem of computing simpler policies for POMDPs, and provide several approaches to still ensure their expressiveness. Key aspects are (1) the combination of an arbitrary number of specifications the policies need to adhere to, (2) a restricted form of randomization, and (3) a light-weight preprocessing of the POMDP model to encode memory. We provide a novel encoding as a mixed-integer linear program as baseline to solve the underlying problems. Our experiments demonstrate that the policies we obtain are more robust, smaller, and easier to implement for an engineer than those obtained from state-of-the-art POMDP solvers.

Real-Time Diagnosis of Configuration Errors for Software of AI Server Infrastructure

Artificial intelligence (AI) server infrastructure has been built to support AI applications and handle data-intensive workloads. AI server infrastructure is the essential building blocks, and errors in AI server infrastructure may lead to fatal consequences to any AI applications built upon it. Compared to traditional software, software for AI server infrastructure is more configurable, and thus more likely to have configuration errors that might prevent correct software behaviors. Previous work on misconfiguration diagnosis requires sufficient execution history or manual intervention, and can hardly diagnose potential misconfigurations which are not triggered at launching. In this paper, we propose a real-time method to address these issues. Specifically, we combine program analysis and real-time log parsing to diagnose configuration errors. It maps each configuration option to the log code by applying program slicing only once, and parses real-time logs during the operation of the AI server without manual intervention. We evaluate the effectiveness of our approach on the core components of Hadoop, an exemplar AI Server Infrastructure Software. The results show that our method mapped more than 80% of the configuration options to log outputs, identified 90% of the configuration read sites as the slicing seeds, and successfully diagnosed about 10% configuration errors that can not be addressed by previous studies.

DTester: Diversity-Driven Test Case Generation for Web Applications

Search-based Test Case Generation (TCG) for web applications suffers from unstable performance and suboptimal test suite problems due to diversity loss. However, previous diversity metrics mainly only focus on client-side models or server-side code, which are prone to low robustness and poor generalization in practical applications. We propose a diversity-driven TCG method DTester, which can maximize behavior exploration and minimize the test suite size while covering more server-side vulnerable paths. Three diversity metrics (i.e. phenotypic coupling, intent coupling and competitiveness) are proposed to measure the underlying relationship between test cases from user behavior, code logic and test execution history. Moreover, a 3-dimensional weight graph is designed to model association among metrics, which provides fine-grained guidance for the genetic algorithm to generate diverse test cases from the client-side behavior model. Our empirical evaluation on five web applications shows that DTester can efficiently and robustly generate better test suites than the state-of-the-art TCG method. The maximum improvement is [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] in efficiency, test suite size, diversity and robustness.

Reproducible and Portable Big Data Analytics in the Cloud

Cloud computing has become a major approach to help reproduce computational experiments. Yet there are still two main difficulties in reproducing batch based big data analytics (including descriptive and predictive analytics) in the cloud. The first is how to automate end-to-end scalable execution of analytics including distributed environment provisioning, analytics pipeline description, parallel execution, and resource termination. The second is that an application developed for one cloud is difficult to be reproduced in another cloud, a.k.a. vendor lock-in problem. To tackle these problems, we leverage serverless computing and containerization techniques for automated scalable execution and reproducibility, and utilize the adapter design pattern to enable application portability and reproducibility across different clouds. We propose and develop an open-source toolkit that supports 1) fully automated end-to-end execution and reproduction via a single command, 2) automated data and configuration storage for each execution, 3) flexible client modes based on user preferences, 4) execution history query, and 5) simple reproduction of existing executions in the same environment or a different environment. We did extensive experiments on both AWS and Azure using four big data analytics applications that run on virtual CPU/GPU clusters. The experiments show our toolkit can achieve good execution performance, scalability, and efficient reproducibility for cloud-based big data analytics.

A pattern catalog for augmenting Digital Twin models with behavior

Abstract Digital Twins are emerging as a solution to build and extend existing software systems to make better use of data produced by physical systems. For supporting the development of Digital Twins, several software vendors are offering dedicated tool support, often referred to as Digital Twin platforms. The modeling capabilities of these platforms are mostly concerned with structural viewpoints, i.e., providing an overview of available components including their current and historical sensor values. However, behavioral viewpoints did not yet receive much attention on these platforms. As behavioral models are often used during the design processes, e.g., for simulation and synthesis, it would be beneficial for having them included in Digital Twin platforms, e.g., for reasoning on the set of possible next actions or for checking the execution history to perform runtime validation. In this paper, we present a catalog of modeling patterns for augmenting Digital Twin models with behavioral models and their corresponding runtime information without requiring any extension of the code bases of Digital Twin platforms. We demonstrate the presented modeling patterns by applying them to the Digital Twin platform offered by Microsoft, in an additive manufacturing use case of a 3D printer in a production line.

Cortically Evoked Movement in Humans Reflects History of Prior Executions, Not Plan for Upcoming Movement.

Human motor behavior involves planning and execution of actions, some more frequently. Manipulating probability distribution of a movement through intensive direction-specific repetition causes physiological bias toward that direction, which can be cortically evoked by transcranial magnetic stimulation (TMS). However, because evoked movement has not been used to distinguish movement execution and plan histories to date, it is unclear whether the bias is because of frequently executed movements or recent planning of movement. Here, in a cohort of 40 participants (22 female), we separately manipulate the recent history of movement plans and execution and probe the resulting effects on physiological biases using TMS and on the default plan for goal-directed actions using a timed-response task. Baseline physiological biases shared similar low-level kinematic properties (direction) to a default plan for upcoming movement. However, manipulation of recent execution history via repetitions toward a specific direction significantly affected physiological biases, but not plan-based goal-directed movement. To further determine whether physiological biases reflect ongoing motor planning, we biased plan history by increasing the likelihood of a specific target location and found a significant effect on the default plan for goal-directed movements. However, TMS-evoked movement during preparation did not become biased toward the most frequent plan. This suggests that physiological biases may either provide a readout of the default state of primary motor cortex population activity in the movement-related space, but not ongoing neural activation in the planning-related space, or that practice induces sensitization of neurons involved in the practiced movement, calling into question the relevance of cortically evoked physiological biases to voluntary movements.SIGNIFICANCE STATEMENT Human motor performance depends not only on ability to make movements relevant to the environment/body's current state, but also on recent action history. One emerging approach to study recent movement history effects on the brain is via physiological biases in cortically-evoked involuntary movements. However, because prior movement execution and plan histories were indistinguishable to date, to what extent physiological biases are due to pure execution-dependent history, or to prior planning of the most probable action, remains unclear. Here, we show that physiological biases are profoundly affected by recent movement execution history, but not ongoing movement planning. Evoked movement, therefore, provides a readout of the default state within the movement space, but not of ongoing activation related to voluntary movement planning.

Enhancing RDM in Galaxy by integrating RO-Crate

Enhancing RDM in Galaxy by integrating RO-Crate

Incrementally building FAIR Digital Objects with Specimen Data Refinery workflows

Incrementally building FAIR Digital Objects with Specimen Data Refinery workflows

Using goal–question–metric to compare research and practice perspectives on regression testing

AbstractRegression testing is challenging because of its complexity and the amount of effort and time it requires, especially in large‐scale environments with continuous integration and delivery. Regression test selection and prioritization techniques have been proposed in the literature to address the regression testing challenges, but adoption rates of these techniques in industry are not encouraging. One of the possible reasons could be the disparity in the regression testing goals in industry and literature. This work compares the research perspective to industry practice on regression testing goals, corresponding information needs, and metrics required to evaluate these goals. We have conducted a literature review of 44 research papers and a survey with 56 testing practitioners. The survey comprises 11 interviews and 45 responses to an online questionnaire. We identified that industry and research accentuate different regression testing goals. For instance, the literature emphasizes increasing the fault detection rates of test suites and early identification of critical faults. In contrast, the practitioners' focus is on test suite maintenance, controlled fault slippage, and awareness of changes. Similarly, the literature suggests maintaining information needs from test case execution histories to evaluate regression testing techniques based on various metrics, whereas, at large, the practitioners do not use the metrics suggested in the literature. To bridge the research and practice gap, based on the literature and survey findings, we have created a goal–question–metric (GQM) model that maps the regression testing goals, associated information needs, and metrics from both perspectives. The GQM model can guide researchers in proposing new techniques closer to industry contexts. Practitioners can benefit from information needs and metrics presented in the literature and can use GQM as a tool to follow their regression testing goals.

Reducing the number of migrated instances during business process change: A graph rewriting approach

Business processes (BPs) are subject to frequent and unpredictable changes caused by new laws or modifications in the business environment, including user requirements. Many running instances are migrated to new BPs during a BP change, even when these instances satisfy the new requirements. We propose a migration strategy to ignore the migration of these instances. We specify the BP using Petri Nets (PNs), and the change including the instance migration using Graph Rewriting Rules (GRRs). We propose two consistency criteria based on the instance execution history and its possible future executions to identify the instances supporting the change. The first criterion ignores the migration of the instances satisfying the new requirements. The second criterion postpones the migration of instances that support the migration until they definitely cannot satisfy the new requirements, where some instances might avoid the states where the migration is mandatory. The proposed approach is implemented through the framework called Flexible and illustrated using two cases of BP changes. Simulation results shows that a large number of instances can be ignored during the migration, 86.34% and 42.35% respectively. The approach is evaluated using five real-life scenarios, where a positive correlation with respect to the complexity is observed.

Methodology for Evaluating Projects Aimed at Service Quality Using Artificial Intelligence Techniques

The quality of the electrical energy distribution service has a significant impact on consumer satisfaction and the guarantee of the right of concession for the distribution companies. For the utility that is the object of the case study, the main continuity of service indicators was at levels below the regulatory limits. Still, due to budget constraints, the forecast of the benefit that improvement or expansion projects bring to continuity indicators must be assertive for a proper direction of investments and decision making. In this work, a methodology for evaluating projects to improve the quality of service was proposed, with the realization of the estimated benefit associated with the reduction in continuity indicators (DEC and FEC), using concepts of artificial neural networks and evolutionary algorithms. The results were obtained from a three-year history of execution of the utility’s projects. Based on the correlation analysis, a variable selection procedure was developed, where the historical values of interruptions by cause were considered as input, and the results of the continuity indicators associated with the types of projects studied form the outputs of the model. The model was developed using an artificial neural network of the multilayer perceptron type. The results obtained by simulating the new methodology presented absolute relative errors 100 times smaller for estimating the benefits of the projects compared to the current method used by the electric power distributor.

Smart Agricultural Futures Market: Blockchain Technology as a Trust Enabler between Smallholder Farmers and Buyers

Smallholder farmers produce over 70% of the world’s food needs. Yet, the socioeconomic conditions of the smallholder farmers are substandard. One of the primary reasons for this unpropitious situation is that they generate modest income by selling their harvest due to the lack of trusted buyers and organized markets. This research explores how technology can enable the trust to reduce transaction-related risks, empowering unknown parties to transact. Blockchain technology has the potential of mitigating transaction-related risks and promoting trust with a tamper-proof history of transactions and automatic execution of smart contracts. Based on blockchain technology to promote trust, this research has discovered a novel approach for smallholder farmers to conduct exchanges by generating social capital as an individual and using that social capital as collateral for financial exchanges when establishing contracts. This approach empowers farmers to trade smart futures contracts on behalf of the expected harvest at a better rate to receive some cash in advance to be used in the cultivation process to produce a high-quality harvest that attracts better rates. It also enables them to perform aggregated marketing with enhanced market linkages that, in turn, assist in increasing margins made by the farmer.

Dynamic reputation, project selection and market efficiency: The importance of small projects

This paper studies the execution of projects under heterogeneous execution abilities and asymmetric information. A higher ability agent, such as a talented entrepreneur, has better execution abilities and this skill advantage is more pronounced for larger and more profitable projects. Reputation, which affects the cost of financing, can be acquired following a successful execution history but may also be inferred when a more talented agent strategically forgoes the execution of certain projects. Our results highlight the role of smaller less-demanding projects in building reputation, revealing ability and increasing profits. These findings carry novel policy implications, as the relative availability of smaller less-demanding projects affects the allocation efficiency of financing and talent and may be required for the subsequent execution of larger more-demanding projects. We show how a change in the availability of such low-NPV projects may generate a significant increase in market efficiency and welfare, an increase that far exceeds their direct-NPV outcome.

Migration in Hardware Transactional Memory on Asymmetric Multiprocessor

In this paper, a system is presented which implements transactions migration to an asymmetric multiprocessor in order to decrease the probability of conflicts and improve execution performance. Applications parallelization makes programming and testing much more difficult, so the goal is to avoid putting additional burden on a programmer. Therefore, the proposed solution should be fully implemented in hardware. In the asymmetric multiprocessor that is analyzed, all cores have the same instruction set, but they are asymmetric in terms of microarchitectural properties, so that N −1 “small” cores are identical, while the $\text{N}^{\mathrm {th}}$ “big” core is different, as it provides better performance and higher capacities of its units. The idea is to perform transaction migration from the “small” core to the “big” one, based on the history of transaction execution. The experiments were performed using a significantly upgraded Gem5 simulator and eight parallel applications from the STAMP benchmark suite. The experimental results show the speedup and the rate of successfully executed transactions for five different multiprocessor configurations, including symmetric and asymmetric multiprocessors with or without transaction migration. The improvement our algorithm achieves for suitable applications is up to 14% (10% on average) in turnaround time compared to the solutions which do not make use of asymmetry for scheduling transactions.

Fully Adaptive Stochastic Handling of Soft-Errors in Real-Time Systems

In the design of real-time systems, it is becoming increasingly important to take soft-error tolerance into account. While hardening techniques such as error detection and error correction enable us to build systems that can better tolerate soft-errors, they are inevitably accompanied with execution time overhead. In order to mitigate the impact of the increased execution time, Chen et al. proposed to identify the (m, k)-constraints of real-time tasks, which demand that at least m jobs out of k consecutive task invocations must be fault-free, and to design hardening policies based on this constraint. In this paper, we propose a new method to design hardening policies that are adaptive and stochastic. At the heart of our method is a new linear program (LP) formulation that finds an adaptive stochastic policy optimizing the CPU utilization. At the design time, we first identify the task set information of a given system, verify the system schedulability, and solve LPs to find an optimal policy. This policy is represented as a look-up table that specifies the stochastic hardening decisions as a function of the past execution history of the system. At the run time, hardening decisions are made simply by looking up this table. The proposed method finds hardening policies that adaptively reacts to the execution history of the system, allowing improvement in the CPU utilization. The method also deviates from the previous approaches’ viewpoint that reliability must be assessed in an all-or-nothing manner, by devising the notion of stochastic hardening policies. We evaluated the effectiveness of the proposed method using various task sets. In a set of 2,050 benchmarks, the system’s CPU utilization was improved by 2.80%-7.16% on average under different configurations. The improvement was by as high as 18.45% in the best benchmark.

Switching controller synthesis for time-delayed hybrid systems

How to design safe and reliable safety-critical cyber-physical systems (CPSs) so thatwe can bet our daily lives on them is a grand challenge to computer science and control theory. Delays in feedback control are ubiquitous, i.e., the behavior evolution of a system depends not only on its current state, but also on its execution history. Obviously, delays may invalidate the stability/safetycertificates obtained by abstracting them away as in the design of modern CPSs normally. In this paper, we study the switching controller synthesis problem of time-delayed hybrid systems, and propose an invariant-based approach by extending the corresponding approach to the design of CPSs without delays. To this end, based on spectral analysis and linearization, we first show that the differential invariant generation problem of delayed dynamical systems can be reduced to computing reachable sets over bounded time horizons; we then propose an abstract-based algorithm to over-approximate the reachable set over a given time bound; finally, we implement a prototypical tool of our approach and illustrate it with examples.

Reducing the Discard of MBT Test Cases

Model-Based Testing (MBT) is used for generating test suites from system models. However, as software evolves, its models tend to be updated, which may lead to obsolete test cases that are often discarded. Test case discard can be very costly since essential data, such as execution history, are lost. In this paper, we investigate the use of distance functions and machine learning to help to reduce the discard of MBT tests. First, we assess the problem of managing MBT suites in the context of agile industrial projects. Then, we propose two strategies to cope with this problem: (i) a pure distance function-based. An empirical study using industrial data and ten different distance functions showed that distance functions could be effective for identifying low impact edits that lead to test cases that can be updated with little effort. We also found the optimal configuration for each function. Moreover, we showed that, by using this strategy, one could reduce the discard of test cases by 9.53%; (ii) a strategy that combines machine learning with distance values. This strategy can classify the impact of edits in use case documents with accuracy above 80%; it was able to reduce the discard of test cases by 10.4% and to identify test cases that should, in fact, be discarded.

ExHero: Execution History-Aware Error-Rate Estimation in Pipelined Designs

The increased variability renders nanometer devices prone to timing errors. Recent work focused on the development of error prediction models for either evaluating the effects of timing errors on applications or guiding the voltage/frequency settings. Such models may have considered the data-dependent excitation of paths, but they have neglected the impact of all the concurrently executed instructions on error occurrence; which may lead to inaccurate error estimation in pipelined designs. To investigate such a limitation, we develop ExHero, a fully automated framework that performs dynamic timing analysis considering the execution history of a number of in-flight instructions. Using ExHero, we first demonstrate that the order and type of instructions within sequences that have length equal to the pipeline depth significantly affect the error rate. When applied to a pipelined floating-point unit, ExHero reveals that existing approaches estimate on average 46.5% and 32.1% lower error rate and absolute error, respectively, than the actual ones.

Computing Dynamic Slices of Feature-Oriented Programs with Aspect-Oriented Extensions

This paper proposes a technique to compute dynamic slices of feature-oriented programs with aspect-oriented extensions. The technique uses a dependence based intermediate program representation called composite feature-aspect dependence graph (CFADG) to represent feature-oriented software that contain aspects. The CFADG of a feature-oriented program is based on the selected features that are composed to form a software product and the selected aspects to be weaved. The proposed dynamic slicing technique has been named feature-aspect node marking dynamic slicing (FANMDS) algorithm. The proposed feature-aspect node marking dynamic slicing algorithm is based on marking and unmarking the executed nodes in the CFADG suitably during run-time. The advantage of the proposed approach is that no trace le is used to store the execution history. Also, the approach does not create any additional nodes during run-time.