Execution Framework Research Articles

A Comparative Analysis of Big Data Frameworks: An Adoption Perspective

The emergence of social media, the worldwide web, electronic transactions, and next-generation sequencing not only opens new horizons of opportunities but also leads to the accumulation of a massive amount of data. The rapid growth of digital data generated from diverse sources makes it inapt to use traditional storage, processing, and analysis methods. These limitations have led to the development of new technologies to process and store very large datasets. As a result, several execution frameworks emerged for big data processing. Hadoop MapReduce, the pioneering framework, set the ground for forthcoming frameworks that improve the processing and development of large-scale data in many ways. This research focuses on comparing the most prominent and widely used frameworks in the open-source landscape. We identify key requirements of a big framework and review each of these frameworks in the perspective of those requirements. To enhance the clarity of comparison and analysis, we group the logically related features, forming a feature vector. We design seven feature vectors and present a comparative analysis of frameworks with respect to those feature vectors. We identify use cases and highlight the strengths and weaknesses of each framework. Moreover, we present a detailed discussion that can serve as a decision-making guide to select the appropriate framework for an application.

Reusable Schedule Design and Execution Framework for Autonomous Mission Management in Space

Aerospace mission autonomy can improve mission capabilities, reduce the burden on ground operators, and prevent mission failure when encountering unexpected situations. However, autonomy comes at the cost of additional complexity for design, development, and testing. This research presents an autonomous mission framework to bridge the gap between existing aerospace mission systems and emerging autonomous paradigms. Traditional aerospace missions without custom autonomy solutions generally follow rigid schedules, and tasks are executed during specified time windows. For these types of scenarios, the only task constraint is the current time. Our proposed framework, called the Schedule Manager, expands on this concept by adding additional constraint checks such as conflict, priority, and resource constraints. Through the design and upload of task schedules, constraint tables, and contingency schedules, the benefits of autonomy can be achieved without deviating significantly from the standard, thereby simplifying the adoption, integration, and safety analysis process. This framework was integrated and tested with NASA’s core Flight System and 42 spacecraft simulator, and run on development boards comparable to flight hardware.

An Efficient Execution Framework of Two-Part Execution Scenario Analysis

Response Time Analysis ( RTA ) is an important and promising technique for analyzing the schedulability of real-time tasks under both Global Fixed-Priority ( G-FP ) scheduling and Global Earliest Deadline First ( G-EDF ) scheduling. Most existing RTA methods for tasks under global scheduling are dominated by partitioned scheduling, due to the pessimism of the -based interference calculation where is the number of processors. Two-part execution scenario is an effective technique that addresses this pessimism at the cost of efficiency. The major idea of two-part execution scenario is to calculate a more accurate upper bound of the interference by dividing the execution of the target job into two parts and calculating the interference on the target job in each part. This article proposes a novel RTA execution framework that improves two-part execution scenario by reducing some unnecessary calculation, without sacrificing accuracy of the schedulability test. The key observation is that, after the division of the execution of the target job, two-part execution scenario enumerates all possible execution time of the target job in the first part for calculating the final Worst-Case Response Time ( WCRT ). However, only some special execution time can cause the final result. A set of experiments is conducted to test the performance of the proposed execution framework and the result shows that the proposed execution framework can improve the efficiency of two-part execution scenario analysis by up to in terms of the execution time.

Linking the dynamics of cognitive control to individual differences in working memory capacity: Evidence from reaching behavior.

We used a technique known as reach tracking to investigate how individual differences in working memory capacity (WMC) relate to the functioning of two processes proposed to underlie cognitive control: a threshold adjustment process that temporarily inhibits motor output in response to signals of conflict and a controlled selection process that recruits top-down control to guide stimulus-response translation. Undergraduates (N = 135) performed two WMC tasks (updating counters and symmetry span) and a reach-tracking version of the Eriksen flanker task. Consistent with previous research using button-press flanker tasks, WMC significantly correlated with response time (RT) performance, with higher WMC scores corresponding to smaller congruency effects. Given that RTs reflect the combined functioning of multiple processes underlying cognitive control, we interpreted this effect to reflect a general link between WMC and both the threshold adjustment process and controlled selection process. We also found a significant association between WMC and participants' reach trajectories, with higher WMC scores corresponding to more direct reach movements on incongruent trials involving stimulus-response overlap with the preceding trial. We interpreted this effect to reflect a more specific link between WMC and the functioning of the controlled selection process. We discuss the observed links between WMC and cognitive control in relation to the unity and diversity of executive functions framework and in relation to the role of prefrontal and striatal dopamine in supporting adaptive cognitive control. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

HySec-Flow: Privacy-Preserving Genomic Computing with SGX-based Big-Data Analytics Framework.

Trusted execution environments (TEE) such as Intel's Software Guard Extension (SGX) have been widely studied to boost security and privacy protection for the computation of sensitive data such as human genomics. However, a performance hurdle is often generated by SGX, especially from the small enclave memory. In this paper, we propose a new Hybrid Secured Flow framework (called "HySec-Flow") for large-scale genomic data analysis using SGX platforms. Here, the data-intensive computing tasks can be partitioned into independent subtasks to be deployed into distinct secured and non-secured containers, therefore allowing for parallel execution while alleviating the limited size of Page Cache (EPC) memory in each enclave. We illustrate our contributions using a workflow supporting indexing, alignment, dispatching, and merging the execution of SGX- enabled containers. We provide details regarding the architecture of the trusted and untrusted components and the underlying Scorn and Graphene support as generic shielding execution frameworks to port legacy code. We thoroughly evaluate the performance of our privacy-preserving reads mapping algorithm using real human genome sequencing data. The results demonstrate that the performance is enhanced by partitioning the time-consuming genomic computation into subtasks compared to the conventional execution of the data-intensive reads mapping algorithm in an enclave. The proposed HySec-Flow framework is made available as an open-source and adapted to the data-parallel computation of other large-scale genomic tasks requiring security and scalable computational resources.

Evolution of artificial intelligence and its impact on human rights: from sociolegal perspective

PurposeThe applications of artificial intelligence (AI) in different sectors have become agendas for discussions in the highest circle of experts. The applications of AI can help society and can harm society even by jeopardizing human rights. The purpose of this study is to examine the evolution of AI and its impacts on human rights from social and legal perspectives.Design/methodology/approachWith the help of studies of literature and different other AI and human rights-related reports, this study has taken an attempt to provide a comprehensive and executable framework to address these challenges contemplated to occur due to the increase in usage of different AI applications in the context of human rights.FindingsThis study finds out how different AI applications could help society and harm society. It also highlighted different legal issues and associated complexity arising due to the advancement of AI technology. Finally, the study also provided few recommendations to the governments, private enterprises and non-governmental organizations on the usage of different AI applications in their organizations.Research limitations/implicationsThis study mostly deals with the legal, social and business-related issues arising due to the advancement of AI technology. The study does not penetrate the technological aspects and algorithms used in AI applications. Policymakers, government agencies and private entities, as well as practitioners could take the help of the recommendations provided in this study to formulate appropriate regulations to control the usage of AI technology and its applications.Originality/valueThis study provides a comprehensive view of the emergence of AI technology and its implication on human rights. There are only a few studies that examine AI and related human rights issues from social, legal and business perspectives. Thus, this study is claimed to be a unique study. Also, this study provides valuable inputs to the government agencies, policymakers and practitioners about the need to formulate a comprehensive regulation to control the usage of AI technology which is also another unique contribution of this study.

An executable framework for modeling and validating cooperative capability requirements in emergency response system

As the scale of current systems become larger and larger and their complexity is increasing gradually, research on executable models in the design phase becomes significantly important as it is helpful to simulate the execution process and capture defects of a system in advance. Meanwhile, the capability of a system becomes so important that stakeholders tend to emphasize their capability requirements when developing a system. To deal with the lack of official specifications and the fundamental theory basis for capability requirement, we propose a cooperative capability requirements (CCR) meta-model as a theory basis for researchers to refer to in this research domain, in which we provide detailed definition of the CCR concepts, associations and rules. Moreover, we also propose an executable framework, which may enable modelers to simulate the execution process of a system in advance and do well in filling the inconsistency and semantic gaps between stakeholders' requirements and their models. The primary working mechanism of the framework is to transform the Alf activity meta-model into the communicating sequential process (CSP) process meta-model based on some mapping rules, after which the internal communication mechanism between process nodes is designed to smooth the execution of behaviors in a CSP system. Moreover, a validation method is utilized to check the correctness and consistency of the models, and a self-fixing mechanism is used to fix the errors and warnings captured during the validation process automatically. Finally, a validation report is generated and fed back to the modelers for system optimization.

FogFrame: a framework for IoT application execution in the fog.

Recently, a multitude of conceptual architectures and theoretical foundations for fog computing have been proposed. Despite this, there is still a lack of concrete frameworks to setup real-world fog landscapes. In this work, we design and implement the fog computing framework FogFrame—a system able to manage and monitor edge and cloud resources in fog landscapes and to execute Internet of Things (IoT) applications. FogFrame provides communication and interaction as well as application management within a fog landscape, namely, decentralized service placement, deployment and execution. For service placement, we formalize a system model, define an objective function and constraints, and solve the problem implementing a greedy algorithm and a genetic algorithm. The framework is evaluated with regard to Quality of Service parameters of IoT applications and the utilization of fog resources using a real-world operational testbed. The evaluation shows that the service placement is adapted according to the demand and the available resources in the fog landscape. The greedy placement leads to the maximum utilization of edge devices keeping at the edge as many services as possible, while the placement based on the genetic algorithm keeps devices from overloads by balancing between the cloud and edge. When comparing edge and cloud deployment, the service deployment time at the edge takes 14% of the deployment time in the cloud. If fog resources are utilized at maximum capacity, and a new application request arrives with the need of certain sensor equipment, service deployment becomes impossible, and the application needs to be delegated to other fog resources. The genetic algorithm allows to better accommodate new applications and keep the utilization of edge devices at about 50% CPU. During the experiments, the framework successfully reacts to runtime events: (i) services are recovered when devices disappear from the fog landscape; (ii) cloud resources and highly utilized devices are released by migrating services to new devices; (iii) and in case of overloads, services are migrated in order to release resources.

Off-chain management and state-tracking of smart programs on blockchain for secure and efficient decentralized computation

In the last decade various crypto-currencies opened new doors to a decentralized electronic monetary system. Emergence of new Blockchain platforms, such as Ethereum, introduced new features for the implementation of decentralized software or smart contracts. The applications of blockchain technology for secure and efficient information systems is rapidly increasing. However, due to the limitations of this platform including high computational and storage cost, it is not possible to run sophisticated and computationally expensive software on the blockchain. In this paper, a new framework for execution of smart programs on blockchain called the Smart Program Runner Framework (SPRF) is proposed. The proposed framework works by moving decentralized applications to the off-chain and only storing the hash of the state of the application on-chain. This framework can be implemented by creating a side-chain to an existing blockchain without any modifications. The proposed framework is implemented on Stellar blockchain and has various applications for secure and efficient information systems and can provide the required platform for execution of computationally demanding software on blockchain.

Fangorn

Pervasive needs for data explorations at all scales have populated modern distributed platforms with workloads of different characteristics. The growing complexities and diversities have thereafter imposed distinct challenges to execute them on shared clusters in corporate or public clouds. This paper presents Fangorn, an adaptive execution framework built on an enriched graph model. As the underlying infrastructure for core computation platforms at Alibaba, Fangorn supports various execution modes and caters to heterogeneous workloads. With the capability to orchestrate graph executions with both long-running and requested-on-demand resources at the same time, Fangorn allows exploration of tradeoffs between latency and resource efficiency, for jobs of all scales. By modeling distributed job executions as mutable graphs with pluggable components, Fangorn offers a systematic framework to adjust job executions adaptively, according to data statistics collected during run-time. Fangorn supports an array of different computation engines ranging from relational to deep learning, and is fully deployed on production clusters across Alibaba. It manages tens of millions of distributed jobs daily, with job size scaling from one to half-million.

How Do We Optimize Risk in Enterprise Architecture when Deploying Emerging Technologies?

Emerging Technologies which merge cyber-physical systems continue to transform businesses and digital agility in transformative ways. Importantly, most investigations around focus on either cyber risk or the risk around physical systems but it does not encompass both. However, the immediate challenge is new opportunities occurring with emerging technologies. Examples include automobiles, the Internet of Things (IoT), medical devices, and building controls. In this study we will focus identifying risk as an optimization not a minimization problem and how to develop a practical approach for executives and boards to use in the oversight of cyber physical systems. Based on interviews with executive leadership teams and boards of directors we explored the over-arching research question: How can we apply a risk-based approach to cyber-physical security and what questions should business leaders be asking? The research methodology used a survey instrument and multiple qualitative methods involving business leaders from 60 companies and 80 business leaders from September 2018 – September 2019. Based on this analysis, we developed an extended framework for executives, as well as questions and process for boards to consider as part of their oversight. The Extended Risk-Based Approach equips boards and executives as they begin to develop their thinking around enterprise cyber physical risk.

A mobile cloud computing framework for execution of data as a service using cloudlet

The mobile device has become an essential utility tool for more effective computation, storage, and power, making it suitable for mobile cloud computing. The cloudlet is used as a connectivity establishment link between the mobile device and the cloud. The objective of this paper is to focus on mobile cloud computing facilitated with cloudlet-based computation. The latter possesses inter-cloudlet communication, which had been proposed within the mobile cloudletbased computing environment framework. The same had been further enhanced to scalable critical parameter yield of resources framework. Nevertheless, this was not taken to the criteria, which would impact the yield factor, in terms of availability. The present research endeavor aims to improve the algorithm by considering some more criterion and provides a new mobile cloud computing framework for data execution as a service using cloudlet. The outcome shows a positive result in the cloud- cloudlet based computation.

A Coupling Framework for Multi-Domain Modelling and Multi-Physics Simulations.

This paper describes a coupling framework for parallel execution of different solvers for multi-physics and multi-domain simulations with an arbitrary number of adjacent zones connected by different physical or overlapping interfaces. The coupling architecture is based on the execution of several instances of the same coupling code and relies on the use of smart edges (i.e., separate processes) dedicated to managing the exchange of information between two adjacent regions. The collection of solvers and coupling sessions forms a flexible and modular system, where the data exchange is handled by independent servers that are dedicated to a single interface connecting two solvers’ sessions. Accuracy and performance of the strategy is considered for turbomachinery applications involving Conjugate Heat Transfer (CHT) analysis and Sliding Plane (SP) interfaces.

Machine learning steered symbolic execution framework for complex software code

AbstractDuring program traversing, symbolic execution collects path conditions and feeds them to a constraint solver to obtain feasible solutions. However, complex path conditions, like nonlinear constraints, which widely appear in programs, are hard to be handled efficiently by the existing solvers. In this paper, we adapt the classical symbolic execution framework with a machine learning approach for constraint satisfaction. The approach samples and learns from different solutions to identify potentially feasible area. This sampling-learning style solving can be applied in different class of complex problems easily. Therefore, incorporating this approach, our framework, MLBSE, supports the symbolic execution of not only simple linear path conditions, but also nonlinear arithmetic operations, and even black-box function calls of library methods. Meanwhile, thanks to the theoretical foundation of the machine learning based approach, when the solver fails to solve a path condition, we can have an estimation of the confidence in the satisfiability (ECS) of the problem to give users insights about how the problem is analyzed and whether they could ultimately find a solution. We implement MLBSE on the basis of Symbolic Path Finder (SPF) into a fully automatic Java symbolic execution engine. Users can feed their code to MLBSE directly, which is very convenient to use. To evaluate its performance, 22 real case programs are used as the benchmarks for MLBSE to generate test cases, which involve a total number of 1042 methods that are full of nonlinear operations, floating-point arithmetic as well as native method calls. Experiment results show that the coverage achieved by MLBSE is much higher than the state-of-the-art tools.

Applying cooperative module pair analysis to uncover compatibility mechanism of Fangjis: An example of Wenxin Keli decoction

Ethnopharmacological relevanceFangji is an ancient combinatorial formula. The compatibility mechanisms that how component herbs of Fangji work cooperatively to achieve the executive framework remain unexplored. Aim of the studyToexplore compatibility mechanism and systematical effects of Fangjis by taking Wenxin Keli decoction (WXKL), a classical Fangji constituted by Codonopsis Radix, PolygonatiRhizoma, Notoginseng Radix Et Rhizoma, Ambrum, and Nardostachyos Radix Et Rhizoma., as example. Main methodsHere, we employed bioinformatics approach, including cluster analysis, cooperative module pair analysis, primary module identification, and proximity examination among target profile of herbs, to investigate compatibility characterization and anti-arrhythmia mechanism of WXKL. Finally, core mechanisms of WXKL were validatedby in vivo experiments. ResultsAs a result, we identified 695 putative target proteins and 27 clusters (W-modules) inWXKL target network (W-network), in which W-module 1, 2, 4, 8, 10 were primary modules. The cooperative module pairs were W-module 2 and 4, W-module 2 and 8, and W-module 2 and 1, all of which existed in Codonopsis Radix- or Notoginseng Radix Et Rhizoma.-condition. And Nardostachyos Radix Et Rhizoma only yielded cooperation between W-module 1 and 2. The proximity of herbs’ target profiles of Codonopsis Radix and Notoginseng Radix Et Rhizoma were similar, and Nardostachyos Radix Et Rhizoma and Ambrum were similar. For the compatibility framework, Codonopsis Radix general regulated 70.67% targets and majority W-modules (81.48%) as sovereign herb, contributing to primary therapeutic effect, mainly involving neurohormonal regulation, vasomotor, inflammation and oxidative stress. Other herbs assisted Codonopsis Radix to enhance major outcomes through common modules, and acted as complementary roles through unique process including mitotic cell cycle, biosynthetic and catabolic process, etc. Furthermore, WXKL regulated 66.67% hub proteins of arrhythmia-network, 68.18% and 47.37% proteins in primary arrhythmia-module 1 and 2, mainly involving ion channel activity, neurohormonal regulation, and stress response processes, to constitute regulatory network focusing on cardiovascular, renal, nervous system, to reverse the pathological process of arrhythmia. In vivo experiments demonstrated WXKL can attenuate adrenergic activation induced sympathetic atrial fibrillation by inhibiting calmodulin expression (CaM) and ryanodine receptor 2 (RYR2) phosphorylation to regulate neurohormonal action. ConclusionThis strategy provided an overarching view of anti-arrhythmia mechanism of WXKL and its internal compatibility, and may facilitate the understanding of compatibility in Fangjis from the perspectives of modern biology.

Interference-aware execution framework with Co-scheML on GPU clusters

Recently, improving the overall resource utilization through efficient scheduling of applications on graphic processing unit (GPU) clusters has been a concern. Traditional cluster-orchestration platforms providing GPUs exclusively for applications constrain high resource utilization. Co-execution of GPU applications is suggested to utilize limited resources. However, the co-execution of GPU applications without considering their diverse characteristics can lead to their unpredictable performances owing to interference resulting from contention and unbalanced usage of resources among applications. This paper proposes an interference-aware execution framework with Co-scheML for various GPU applications such as high performance computing (HPC), deep learning (DL) training, and DL inference. Various resource-usage characteristics of GPU applications are analyzed and profiled to identify various degrees of their application interference. As interference prediction is challenging owing to the complexity of GPU systems, an interference model is generated by applying defined GPU metrics to machine learning (ML) models. A Co-scheML scheduler deploys applications to minimize the interference using the predicted interference from the constructed model. Experimental results of our framework demonstrated that the resource utilization improved by 24%, the average job completion time (JCT) improved by 23%, and the makespan shortened by 22% on average, compared to baseline schedulers.

The Sebi Role in Creating Awareness about Investor Protection and Education

Colossal expansion in the monetary market tasks, unusual expands in the awareness levels of the clients about the monetary items and the dangers related with them, mounting up of the quantity of monetary fakes, strategies that the organizations use to procure benefits, and numerous different components become the test for the endurance of the monetary and capital business sectors all around. A need emerged for the monetary controllers to present the idea of Investor Protection and Investor Education to shield their wellbeing. The monetary emergency likewise had underlined on the conspicuousness of the monetary mindfulness and security to cradle financial backers from the market disturbances. Indian economy is perceived as the most dependable one for interests in various roads because of its solid legal executive framework. Financial backer assurance is significant for any venture. Something else, no market can support and develop. Protections and Exchange Board of India is a market controller of India as we probably are aware and one of its goals is to teach and secure the financial backers. Likewise, SEBI is perceived as the most regarded controller among the controllers in India. Since 1992, SEBI as an independent body making rules and guidelines and modifying them according to the changing business sector elements and securing financial backers fretfully till now. Regardless of every one of its endeavors, there are a few holes between financial backer insurance and financial backer fulfillment. The current examination is a portrayal of a coordinated writing audit with trustworthy referring to, and ends that were drawn from various coherent creek of speculations identifying with the investigation "Financial backer Protection: Awareness among Professionals.

A fully distributed multi-robot navigation method without pre-allocating target positions

This study focuses on the multi-robot navigation problem with unpredictable state transition disturbance. The primary goal is to construct a fully distributed multi-robot navigation method without pre-allocating target positions. To this aim, a reinforcement learning based method is presented, in which a distribution of state transition module is proposed to guarantee adaptiveness when trained policies are applied in physical multi-robot systems. The method incorporates a centralized training but fully distributed execution framework. The former can eliminate non-stationarity of the environment, and the latter enables the robots to collaboratively handle partially observable scenarios. Mean while, the designed reward function can guide the robots to approach not pre-allocated target positions and the nearly optimal trajectories are achieved in continuous environment. After training, the robots make decisions independently, coordinate, and cooperate with each other to determine the next actions from their current positions before arriving in target positions without pre-allocation, in which the trajectories are nearly optimal with partial observation available for each robot. Simulations are performed with increasingly complex environments, such as the addition of static obstacles and randomly moving obstacles. The results show that the robots are able to achieve the primary goal with different state transition disturbance, which demonstrates the feasibility, effectiveness, and robustness. Furthermore, experiments are carried out using our multi-robot system corresponding to the simulation. The experimental results demonstrate the effectiveness and robustness of the proposed navigation method to handle a variety of typical robotic scenarios.

Parallelism in Autonomous Robotic Surgery

Robots can perform multiple tasks in parallel. This work is about leveraging this capability in automating multilateral surgical subtasks. In particular, we explore, in a simulation study, the benefits of considering this parallelism capability in developing execution models for autonomous robotic surgery. We apply our work to two surgical subtask categories: (i) coupled-motion subtasks, where multiple robot arms share the same resources to perform the subtask, and (ii) decoupled-motion subtasks, where each robot arm executes its part of the task independently from the others. We propose and develop parallel execution models for the surgical debridement subtask, a representative of the first category, and the multi-throw suturing subtask, a representative of the second one. Comparing these parallel execution models to the state-of-the-art ones shows significant reductions in the subtasks completion time by at least 40%. In 20 trials, our results show that our proposed model for the surgical debridement subtask, that uses hierarchical concurrent state machines, provides a parallel execution framework that is efficient while greatly reducing collisions between the arms compared to a naive parallel execution model without coordination. We also show how applying parallelism can lead to execution models that go beyond the normal practice of human surgeons. We finally propose the notion of “automation for surgical manual execution” where we argue that autonomous robotic surgery research can be used as a tool for surgeons to discover novel manual execution models that can significantly improve their surgical practice.

Evaluating Automatic Program Repair Capabilities to Repair API Misuses

API misuses are well-known causes of software crashes and security vulnerabilities. However, their detection and repair is challenging given that the correct usages of (third-party) <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">api</small> s might be obscure to the developers of client programs. This paper presents the first empirical study to assess the ability of existing automated bug repair tools to repair <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">api</small> misuses, which is a class of bugs previously unexplored. Our study examines and compares 14 Java test-suite-based repair tools (11 proposed before 2018, and three afterwards) on a manually curated benchmark ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">APIRepBench</small> ) consisting of 101 <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">api</small> misuses. We develop an extensible execution framework ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">APIARTy</small> ) to automatically execute multiple repair tools. Our results show that the repair tools are able to generate patches for 28 percent of the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">api</small> misuses considered. While the 11 less recent tools are generally fast (the median execution time of the repair attempts is 3.87 minutes and the mean execution time is 30.79 minutes), the three most recent are less efficient (i.e., 98 percent slower) than their predecessors. The tools generate patches for <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">api</small> misuses that mostly belong to the categories of missing <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">null</monospace> check, missing value, missing exception, and missing call. Most of the patches generated by all tools are plausible (65 percent), but only few of these patches are semantically correct to human patches (25 percent). Our findings suggest that the design of future repair tools should support the localisation of complex bugs, including different categories of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">api</small> misuses, handling of timeout issues, and ability to configure large software projects. Both <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">APIRepBench</small> and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">APIARTy</small> have been made publicly available for other researchers to evaluate the capabilities of repair tools on detecting and fixing <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">api</small> misuses.