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Application Programming Interfaces (APIs) have become one of the key assets within modern businesses, facilitating the linking and integration of intra- and inter-organizational data and systems in the context of complex and heterogeneous technology ecosystems. APIs allow organizations to monetize data, build profitable partnerships and foster innovation and growth. Understanding APIs and their usage are therefore key to building solutions for enabling successful business operations. This paper aims at understanding API topic issues posted on Stack Overflow (SO), a Community Question Answering (CQA) site for programmers. We conduct an empirical analysis on a sample of 400 randomly-selected Q&As threads to help identify API-related issues and their main topics. A thematic analysis performed on this sample reveals eight main topics related to APIs, among which API usage, debugging, API constraints and API security emerged as the major ones. We also exemplify the types of support provided by SO community in addressing each of the identified topics and discuss possible venues on how to further leverage this knowledge.

Application Programming Interface (API) is a core technology that facilitates developers' productivity by enabling the reuse of software components. Understanding APIs and gaining knowledge about their usage are therefore fundamental needs for developers that impact a wide range of software development activities. This paper presents an approach to enable API users to explore, discover and learn about APIs through API topic issues discussed in Stack Overflow (SO), a widely used programming, community question-answering (CQA) site. Our work proposes an integrated API Knowledge Base (KB) and indexing technique that combines both SO API-related posts as well as other API learning resources collected from the Web (e.g., API video-tutorials from Youtube). The resulting indexed and enriched API community knowledge can be queried in a API-topic-issue driven manner using a simple yet powerful domain-specific language (DSL). We demonstrate the feasibility of our approach through Scout-bot, our tool for exploration and discovery of API topic issues.

There have been ever-increasing amounts of security vulnerabilities discovered and reported in recent years. Much of the information related to these vulnerabilities is currently available to the public, in the form of rich, textual data (e.g. vulnerability reports). Many of the state-of-the-art techniques used today to process such textual data rely on so-called word embeddings. As of today, several pre-trained embeddings have been created, many of which rely on general-purpose training datasets such as Google News and Wikipedia. More recently, other domain-specific word embeddings have been created (e.g. in the context of software development) to cope with terminology and ambiguity limitations of existing general-purpose embeddings. The availability of word embeddings for specialised domains is critical for the effectiveness of domain-specific tasks that rely on this technique. In this paper, we propose a word embedding for the cyber security vulnerability domain. We train our embedding model on multiple, rich and heterogeneous security vulnerability information sources publicly available on the web. The benefits of such specialised word embedding are demonstrated through a qualitative comparison of word similarity and the exemplary task of matching security professionals to vulnerability discovery tasks posted to bug bounty programs. We also introduce a new dataset of words pairs similarity with a human judgement that can be used as a benchmark. Our experimental results show that, in the context of cyber security, our domain-specific word embedding outperforms existing pre-trained embeddings built on general-purpose and software engineering datasets.

© 2019 IEEE. The prevalence of online social media facilitates massive knowledge acquisition and sharing throughout the Web. Meanwhile, it inevitably poses the risk of generating and disseminating false information by both benign and malicious users. Despite there has been considerable research on false information detection from both the opinion-based and fact-based perspectives, they mostly focus on tailored solutions for a particular domain and carry out limited work on leveraging multi-faceted clues such as textual cues, behavioral trails, and relational connection. We propose a novel dual-stream attentive random forest that is capable of selecting clues of discriminative information from individuals, collective information (e.g., texts), and correlations of entities (e.g., social interactions) adaptively. In particular, we use an interpretive attention model for learning textual contents. The model treats the important and unimportant content differently when constructing the textual representation and employs a multilayer perceptron to capture the hidden complex relationships among features of side information. We further propose a unified framework for leveraging the above clues, where we use attentive forests to provide probabilistic distribution as predictions over the two learned representations, which are then leveraged to make a better estimation. We conduct extensive experiments on three real-world benchmark datasets for fake news and fake review detection. The results show our approach outperforms multiple baselines in the accuracy of detecting false information.

The Industry 4.0 approach purpose the massive transformation of the traditional plants into smart factories, however the current industrial shop floor, is composed of a large number of dumb devices (legacy devices). This work describes an approach for integrating legacy devices with a cloud-based IoT platform. This proposal uses the following steps: Virtualize the real equipment, connect the virtual equipment to the cloud, make all the necessary tests with the virtual equipment, and after testing it connects the real equipment with the cloud. We opted for the initial use of a virtual equipment (virtual Modular Production System-CIROS®), as this creates a totally free and secure development and a testing environment without having to stop the real equipment in the shop floor for testing. In this approach were used the same tools that the companies use, such as OPC UA, UaGateway, CODESYS®, and the integration was carried out with SAP IoT Cloud Platform.

Question answering (Q&A) communities have gained momentum recently as an effective means of knowledge sharing over the crowds, where many users are experts in the real-world and can make quality contributions in certain domains or technologies. Although the massive user-generated Q&A data present a valuable source of human knowledge, a related challenging issue is how to find those expert users effectively. In this paper, we propose a framework for finding such experts in a collaborative network. Accredited with recent works on distributed word representations, we are able to summarize text chunks from the semantics perspective and infer knowledge domains by clustering pre-trained word vectors. In particular, we exploit a graph-based clustering method for knowledge domain extraction and discern the shared latent factors using matrix factorization techniques. The proposed clustering method features requiring no post-processing of clustering indicators and the matrix factorization method is combined with the semantic similarity of the historical answers to conduct expertise ranking of users given a query. We use Stack Overflow, a website with a large group of users and a large number of posts on topics related to computer programming, to evaluate the proposed approach and conduct extensively experiments to show the effectiveness of our approach.

With the advent of big data era, the data analytical applications have sprung up which can be modeled using workflow. Such workflow applications are subject to continuously arriving requests and have a rigid requirement on response time. When running the analytical workflow in a cloud platform, one of the critical questions arising here is that how to provision resources in a way that the monetary cost can be reduced under the constraint of the response time. In this paper, we use queueing network theory to address this challenge. First, we present the performance analytic model for the elastic analytical workflows based on queueing network theory. Then, we design a resource provision strategy to determine the number of virtual machines for individual component of the applications with response time constraint. Simulation experiments using the real workload trace data show that our proposed approach provides a simple yet powerful solution to provision resources for analytical workflows under dynamic workload conditions.

In crowd-sourcing, selecting the person with suitable expertise is very important; especially since the task requester is not always in direct contact with the worker. Recently, this has become increasingly important particularly when the crowd-sourced tasks are complex and require skillful workers (e.g. software development, software testing, vulnerability discovery, and open innovation). In this paper, we aim to identify indicators to determine the expertise of security professionals in a crowd-sourcing vulnerability discovery platform. We review literature and online contents, conduct interviews with domain experts, and survey security professionals involved in the task of vulnerability discovery. We discuss the indicators we have found, and we provide some recommendations to help improve the process of selecting security professionals to perform crowd tasks related to vulnerability discovery.

This paper presents the results of a systematic literature review (SLR) of the advantages and challenges of using open source (OS) in computer science education. The review highlights an innovative approach of requesting students of computer science to become active members of existing OS communities as part of their curriculum. The acquisition of a wide range of skills, increasing student motivation, support for contextual learning and student-centered courses as well as the availability of a wealth of data to inform and support decision-making by educators are the main advantages as identified by the SLR. Similarly, high barriers to entry in OS projects, difficulties related to student support, assessments, grouping of students and choice of an adequate OS project are the potential challenges. In this paper, the advantages and challenges of using OS in an existing framework of a course design and delivery have been discussed in detail. In particular, the potential impact of OS in computer science education curriculum has been explored from the perspective of education providers.

The proliferation of tools for different aspects of cloud resource configuration processes encourages DevOps to design end-to-end and automated configuration processes that span across a selection of best-of-breed tools. But heterogeneities among configuration knowledge representation models of such tools pose vital limitations for acquisition, discovery and curation of configuration knowledge for federated cloud application and resource requirements. We propose an embryonic data-model for representing cloud resource configuration knowledge artifacts. We also propose a rule based recommender service, which empowers (1) incremental knowledge acquisition and curation, and (2) declarative context driven knowledge recommendation. The paper describes the concepts, techniques and current implementation of the proposed system. Experiments on 36 real-life cloud resources show efficient re-use of configuration knowledge by our approach compared to traditional techniques.

Workers in online crowd sourcing systems have different levels of expertise, trustworthiness, incentives and motivations. Therefore, recruiting sufficient number of well-suited workers is always a challenge. Existing methods usually recruit workers through open calls, friendships relations, matching their profiles with task requirements or recruiting teams of workers. But there are still challenges that need more investigations, mainly all existing recruitment methods are highly vulnerable to collaborating misbehaviour, i.e., Collusion. %These groups are highly vulnerable to collusion attacks. In this paper, we propose a recruitment method which takes into account individual and social attributes of workers to find suitable workers. The method discovers indirect collaborations between workers to harness implicit teamwork knowledge in order to increase the quality of crowd sourcing tasks' outcome and in the same time prevent collusion attacks. The proposed method is implemented and tested using the simulated data, build based on a public data dump from Stack overflow. The evaluation results show the accuracy of the obtained results and superiority of our proposed method over the other related work.

The number of Web-Services publicly accessible through APIs have rapidly grown in recent years. Although, while these services are key in providing access to data as well as a variety of functionality, often their full potential remains yet to be fully exploited. Due to the different standards used to implement and expose Web services, it is usually cumbersome for developers to reuse them within third-party applications. In this paper, we present Service Graph Base, a unified graph-based platform for representing and manipulating service artifacts. It allows professional developers to add service knowledge to a graph base, which can be done either automatically or manually. By adopting the language-neutral RDF-based Graph Model and developing a uniformly-interfaced service editor for service native artifacts extraction, services are represented as RDF graphs in Service Graph Base. Once they are in the graph base, common SPARQL queries can be used to provide intelligent exploration over the base, thereby providing a unified approach regardless of the actual service type. In our work, we have harvested 4,925 Yahoo! pipes and 300 WSDLs for service artifacts extraction and service query. Experiments showed that 99.35% Yahoo! pipes and 100% WSDLs were successfully represented as RDF graphs.

Worker selection is a significant and challenging issue in crowdsourcing systems. Such selection is usually based on an assessment of the reputation of the individual workers participating in such systems. However, assessing the credibility and adequacy of such calculated reputation is a real challenge. In this paper, we propose a reputation management model which leverages the values of the tasks completed, the credibility of the evaluators of the results of the tasks and time of evaluation of the results of these tasks in order to calculate more dependable quality metrics for workers and evaluators. The model has been implemented and experimentally validated.

International audience; Business process management, service-oriented architectures and software back-engineering heavily rely on the fundamental processes of mining of processes and web service business protocols from log files. Model extraction and mining aim at the (re)discovery of the behavior of a running modelimplementation using solely its interaction and activity traces, and no a priori information on the target model. This paper presents an approach for correlating messages and extracting the business protocol of a web service in the realistic scenario in which correlation information is entirely absent from interaction and activity logs. Correlation is achieved through deterministic computations that result in an extremely efficient method whose extensive experiments have shown its solid reliability, robustness when dealing with complex structures, and very high performance and scalability. This approach and the underlying algorithms extend what is actually possible to achieve in the web service business protocol mining domain using incomplete and noisy data logs, and opens new horizons in back-engineering of web services. The theoretical and experimental results clearlyshow the leap forward achieved herein.

Duplicate records are a major problem and duplicate invoices are a specific example of this. The detection of duplicate invoices is a critical issue for business since duplicate invoices can result in a company paying more than once for goods or services ordered. Past experience has shown that generic duplicate record detection techniques are not very useful when applied to invoices: the rate of false positives can be so high that invoice clerks are discouraged from using the system. This is because such approaches do not take the business context into account, e.g. what types of good were ordered as well as the past relationship with that vendor. In this paper, we discuss applying Ripple Down Rules (RDR), an approach for incremental and end-user-centred knowledge acquisition, to the problem of classifying pairs of potential duplicate invoices. We describe how we built a prototype on top of the SAP ERP product and evaluated it on a real data set that had been previously independently audited for duplicates. The preliminary results have highlighted the significant potential of this approach for assisting invoicing clerks processing potential duplicate invoices. We have observed a drop in the rate of false positives from 92% down to 18.66% when compared to traditional approaches that do not take the business context into account. We suggest that incremental development of domain specific knowledge may have more general application to the problem of handling duplicate records.

This paper deals with the problem of discovering protocol models by analyzing real-world interaction logs. There are several scenarios where protocol discovery is useful and needed: (i) In practice, the protocol definition may not be available. This can happen for many reasons, e.g., the service has been developed using a bottom-up approach, by simply SOAP-ifying an existing application; (ii) even when the protocol model is available, protocol discovery is important as we may want to verify if the designed protocol model is what is actually being supported by the implementation and, if not, what are the differences. An instance of this problem involves discovering if the service is compliant with the protocol specification required by some domain-specific standardization body or industry consortium.

Process-based composition of Web services has recently gained significant momentum in the implementation of business processes. A critical and time-consuming part of business process development is the detection and handling of exceptions that may occur during process execution. In this paper, we introduce a novel, policy-driven approach to exception management, which substantially simplifies business process development. We advocate that exception management should be implemented in the system infrastructure. Using our exception management framework, developers define exception policies in a declarative manner. Before a business process is executed, the service composition middleware integrates the exception policies with normal business logic to generate an exception-aware process schema. We argue that our policy-driven approach significantly reduces the development time of business processes through its separation of the development of the business logic and the exception handling policies.

Onboard mobile networking is a new communica- tion paradigm where in-vehicle mobile router (MR) connects a large number of passengers to the Internet via cellular (or satellite) networks. All communication to and from the public transport is transparently handled by the MR. In this commu- nication model, a call blocking of the MR would immediately disrupt on-going sessions of potentially large number of on- board passengers. This observation motivates us to investigate techniques that can reduce, and possibly regulate, call blocking probability of MRs. In this paper, we investigate two different techniques to achieve this objective. The first technique, called ca- pacity reservation (CR), reserves a portion of the total base station capacity for exclusive use by the MRs, thus giving them priority over other calls. In the second technique, called probabilistic admission control (PAC), no capacity is reserved, but other on- going calls in the base station can be interrupted to accommodate arrival of MR calls. PAC regulates the interruptions in the system by probabilistically admitting other calls. Performance of our control algorithms are evaluated and compared by discrete event simulation.

Increasing demand on the scarce radio spectrum indicates that availability of required radio resources (e.g. wireless bandwidth) at a given time and location will be less certain in the future. Resource uncertainty inhibits many future applications that require large amount of resources to be guaranteed at specific times and locations. In order to address the problem of resource uncertainty, these applications may need to reserve network resources in advance. However, in order to support percall advance reservation, the network must store, process, and access large volume of reservation data efficiently. In particular, the admission control functions must have fast and scalable access to these reservation data for making effective decisions regarding the acceptance and rejection of both immediate and future calls. In this paper, we propose a distributed reservation database architecture that features proactive processing and delivery of reservation data to admission control functions located in each radio base-station. To demonstrate the effectiveness of our architecture, we present results from a prototype experiment that compared the proposed proactive approach with the traditional query-based data delivery approach. With proactive approach, the response time of admission control was 20 times or more faster than traditional query-based approach in our experiments.

Architecting distributed software applications is a complex design activity. It involves making decisions about a number of inter-dependent design choices that relate to a range of design concerns. Each decision requires selecting among a number of alternatives; each of which impacts differently on various quality attributes. Additionally, there are usually a number of stakeholders participating in the decision-making process with different, often conflicting, quality goals, and project constraints, such as cost and schedule. To facilitate the architectural design process, we propose a quantitative quality-driven approach that attempts to find the best possible fit between conflicting stakeholders' quality goals, competing architectural concerns, and project constraints. The approach uses optimization techniques to recommend the optimal candidate architecture. Applicability of the proposed approach is assessed using a real system.

Context-aware Web services are emerging as a promising technology for the electronic businesses in mobile and pervasive environments. Unfortunately, complex context-aware services are still hard to build. In this paper, we present a modeling language for the model-driven development of context-aware Web services based on the Unified Modeling Language (UML). Specifically, we show how UML can be used to specify information related to the design of context-aware services. We present the abstract syntax and notation of the language and illustrate its usage using an example service. Our language offers significant design flexibility that considerably simplifies the development of context-aware Web services.

Software architecture design is widely recognized to be a complex task. This is especially true when designing enterprise applications that require deciding about a number of architectural design issues, often involving selecting among various design alternatives that impact differently on a set of quality attributes. In order to facilitate the selection process, earlier research efforts have already investigated the use of quantitative decision-making methods for scoring and ranking design alternatives. These methods, however, treat individual architectural decisions independently without considering their synergistic interrelationships. We argue that many architectural decisions are highly interdependent with each other, and thus need to be treated jointly in the selection process. To support this claim, we have identified two types of dependencies that can occur among different design decisions. We show that in particular situations, these dependencies require employing unconventional methods in determining the appropriate design solution. For this purpose, we suggest formulating the architectural design task as a search problem, and also show how search trees can be useful in this regard. We apply these ideas on a financial system, in order to demonstrate their applicability in a real setting.

The area of finance has always evolved along side with the development of new technology. For instance, utilizing new technologies in capital markets trading automation is one of the major factors for the market efficiency and competitiveness as time has a huge impact on the costs incurred by financial institutions. Considering capital markets as our case study, we investigate the usability of these technologies in implementing business processes that span across a number of legacy applications. We describe Web services as emerging technologies that facilitate the composition and execution of distributed business processes. We also present an overview of a service-oriented architecture for capital market systems (CMSs). This architecture is meant to integrate existing legacy applications and facilitate the automation of trading-related business processes.

We describe an architecture called WebFINDIT that allows dynamic couplings of Web accessible databases based on their content and interest. We propose an implementation using WWW, Java, JDBC, and CORBA's ORBs that communicate via the CORBA's IIOP protocol. The combination of these technologies offers a compelling middleware infrastructure to implement fluid-area enterprise applications. In addition to a discussion of WebFINDIT's core concepts and implementation architecture, we also discuss an experience of exiting WebFINDIT in a healthcare application.