With the advancement of deep neural networks, machine translation has seen rapid progress in recent years. Individuals often rely on machine translation software to facil-itate various tasks. However, the intricacies of neural networks can lead to translation errors, resulting in misunderstandings or conflicts. The most common method for testing machine translation is metamorphic testing. However, metamorphic testing at the phrase or sentence hierarchy may result in some test cases being incorrectly identified as failures. To mitigate this issue, we added the word hierarchy. We proposed a multi-hierarchy metamorphic testing method, MHT, to test machine translation. Hyphenated words as a specific format prone to translation errors, which are chosen as our research object. Based on the common notion that translations of words within the same sentence should be similar, we extract contents from different hierarchies within sentences containing hyphenated words and compare the similarity of their corresponding translations for these specific words. We conducted the experiments on 881 sentences leveraging Google Translate, Microsoft Bing Translator, and Baidu Translate, which detected 111, 91, and 111 suspicious errors with high precision (78.4 %, 82.4 %, and 81.1 %). Translation errors mainly include mis-translation, under-translation, over-translation, and non-translation.

High-quality Artificial intelligence (AI) software in different domains, like image recognition, has been widely emerged in our lives.They are built on machine learning models to implement intelligent features.However, the current research on image recognition software rarely discusses test questions, clear quality requirements, and verification methods.This paper presents a case study of a realistic image recognition application called Calorie Mama using manual and automation testing with a 3D decision table.The study results indicate the proposed method is feasible and effective in quality evaluation.

Business process specified in Business Process Execution Language (BPEL), which integrates existing services to develop composite service for offering more complicated function, is error-prone. Verification and testing are necessary to ensure the correctness of business processes. SPIN, for which the input language is PROcess MEta-LAnguage (Promela), is one of the most popular tools for detecting software defects and can be used both in verification and testing. In this paper, an automatic approach is proposed to construct the verifiable model for BPEL-based business process with Promela language. Business process is translated to an intermediate two-level representation, in which eXtended Control Flow Graph (XCFG) describes the behavior of BPEL process in the first level and Web Service Description Models (WSDM) depict the interface information of composite service and partner services in the second level. With XCFG of BPEL process, XCFGs for partner services are generated to describe their behavior. Promela model is constructed by defining data types based on WSDM and defining channels, variables and processes based on XCFGs. The constructed Promela model is closed, containing not only the BPEL process but also its execution environment. Case study shows that the proposed approach is effective.

Cleanliness of city streets has an important impact on city environment and public health. Conventional street cleaning methods involve street sweepers going to many spots and manually confirming if the street needs to be clean. However, this method takes a substantial amount of manual operations for detection and assessment of street’s cleanliness which leads to a high cost for cities. Using pervasive mobile devices and AI technology, it is now possible to develop smart edge-based service system for monitoring and detecting the cleanliness of streets at scale. This paper explores an important aspect of cities — how to automatically analyze street imagery to understand the level of street litter. A vehicle (i.e. trash truck) equipped with smart edge station and cameras is used to collect and process street images in real time. A deep learning model is developed to detect, classify and analyze the diverse types of street litters such as tree branches, leaves, bottles and so on. In addition, two case studies are reported to show its strong potential and effectiveness in smart city systems.

Rainfall forecasting is crucial in the field of meteorology and hydrology. However, existing solutions always achieve low prediction accuracy for short-term rainfall forecasting. Atmospheric forecasting models perform worse in many conditions. Machine learning approaches neglect the influences of physical factors in upstream or downstream regions, which make forecasting accuracy fluctuate in different areas. To improve the overall forecasting accuracy for short-term rainfall, this paper proposes a novel solution called Dynamic Regional Combined short-term rainfall Forecasting approach (DRCF) using Multi-layer Perceptron (MLP). First, Principal Component Analysis (PCA) is used to reduce the dimension of thirteen physical factors, which serves as the input of MLP. Second, a greedy algorithm is applied to determine the structure of MLP. The surrounding sites are perceived based on the forecasting site. Finally, to solve the clutter interference which is caused by the extension of the perception range, DRCF is enhanced with several dynamic strategies. Experiments are conducted on data from 56 real-world meteorology sites in China, and we compare DRCF with atmospheric models and other machine learning approaches. The experimental results show that DRCF outperforms existing approaches in both threat score (TS) and root mean square error (RMSE).

Advancement in mobile, cloud technologies and IoT has made the world even smaller and connected like never before.It has become a challenge and an opportunity for cities to leverage these growing technologies to solve real city administration problems.Cities are in the transformation state to become state of the art smart-city using these technologies.This paper is about the automation of street cleanliness assessment in near real-time.It answers the question of how can we assess the status of streets more efficiently and effectively.In order to address the problem, this paper proposes a multiple-level assessment service system on how the cleanliness status of streets is collected using mobile stations, connected via city network, analyzed in the cloud and presented to city administrators online or on mobile.The real applications show the usability and feasibility of our system.This also gives opportunities to city residents to participate and contribute towards making the city a better place.

Precise precipitation forecast can better reflect the changing trend of climate, provide timely and efficient environmental information for management decision, as well as help people to make preparations for the incoming floods or droughts. However, existing approaches have limited ability to fore cast future precipitation in different regions. In order to addess the problem, this paper proposes a big data based approach for precipitation forecasting based on deep belief nets, called DBNPF (Deep Belief Network for Precipitation Forecast). The proposed approach can not only learn the hierarchical representation of raw data using a highly generalized way, but also make a more accurate description of the rule underlying different kind of environmental factors. A set of dedicated experiments with hydrological multivariate time series from four typical areas of China is conducted to validate the feasibility and robustness of the model. In the experiments, environmental factors, filtered by factor analysis, are used as input vector, and the next 24 hours precipitation is used as the output vector. We compare DBNPF with other traditional machine learning approaches. The experimental results show that the proposed approach is more robust than other approaches and can also improve the forecast precision.

Advancements in mobile, cloud computing and other techniques have made the world even smaller and connected like never before. It has become a challenge and an opportunity for cities to leverage these growing technologies to solve real city administration problems. Cities are in the transformation to become state-of-the-art smart cities using these technologies. This paper is about the automation of street cleanliness assessment in near real-time. It answers the question of how can we assess the status of streets in a more efficient and effective way. To address the problem, this paper proposes a multi-level assessment system on how the cleanliness status of streets is collected using mobile stations. They are connected via city network, analyzed in the cloud and presented to city administrators online or on mobile. The real case studies show the usability and feasibility of our system. This also gives opportunities for city residents to participate and contribute to making the city a better place.

With the wide-spread usage of Android systems, Android applications have become the target of mobile malwares. Therefore, an effective verification approach for Android applications is essential. Runtime verification based on monitors is one of the most promising techniques to check the software’s behaviors. Currently, there are a number of monitoring approaches for Android applications. However, these approaches mainly have two limitations. First, there is no effective mechanism to reduce the load on Android applications caused by monitors. Second, these approaches mainly rely on logic-based specifications, which are complex and not intuitive for being used in practice. In order to address these two limitations, this paper proposes a scenario-based runtime verification approach for Android applications (called Android-SRV). The approach concentrates on providing a dynamic and usable solution for monitoring applications during execution with the help of aspect-oriented programming. It will translate the scenario-based specification (called property sequence charts (PSCs)) into multi-valued automata (MAs), and then generate AspectJ codes from the MA. Finally, these AspectJ codes are weaved into the target system as monitors. Considering the impact of the monitors on performance load in the Android system, the paper proposes local and cloud collaboration mechanisms to reduce the impact. The scenario-based specification is more intuitive than the traditional logic-based specifications. The approach is shown to be an effective means for monitoring Android applications with controlled overload.

With the rapid advance of mobile computing technology and wireless networking, there is a significant increase of mobile subscriptions.This brings new business requirements and demands in mobile software testing, and causes new issues and challenges in mobile testing and automation.As there are multiple platforms for diverse devices, engineers suffer from the different scripting languages to write platform-specific test scripts.In addition, a unified automation infrastructure is not offered with the existing test platform.This paper proposes a novel approach to mobile application testing based on natural language scripting.A Java-based test script generation approach is developed to support executable test script generation based on the given natural language-based mobile app test operation scripts.A prototype tool is implemented based on some open sources.Finally, the paper reports empirical studies to indicate the feasibility and effectiveness of the proposed approach.

Face recognition system is a widely-used intelligent application nowadays.Existing recognition system evaluation methods primarily focus on recognition rate, i.e., the correct result.However, current research seldom focuses on the quality evaluation of face recognition systems.They seldom consider accuracy or the quality of recognition.To address this issue, this paper proposes several quality factors for evaluation.In addition, corresponding metrics for diverse quality factors are illustrated.Moreover, the paper presents an experimental study on a realistic non-trial face age recognition system using the proposed quality evaluation method.The study result shows the proposed method is feasible and effectiveness in quality evaluation.

Cloud computing has become the most popular computing architecture in the world.The multiplicity of available platforms for end users makes it very inconvenient for service providers to manage, calling for a unified approach for resource management and security in virtualized data centers in order to achieve seamless cloud management.In this paper, we present data center management as a service (DMaaS) as such a unified approach, using multi-tenant software.Virtualized data centers pool cloud resources used to meet enterprise needs, mostly related to storage and processing.The proposed work provides a service model for the cloud infrastructure and supports multiple tenants to connect their data center management in a shared and secure manner.Our service manages disparate virtual environments using a single interface.

With the fast advance of big data technology and analytics solutions, building high-quality big data computing services in different application domains is becoming a very hot research and application topic among academic and industry communities, and government agencies.Therefore, big data based applications are widely-used currently, such as recommendation, predication, and decision system.Nevertheless, there are increasing quality problems resulting in erroneous testing costs in enterprises and businesses.Current research work seldom discusses how to effectively validate big data applications to assure system quality.This paper focuses on big data system validation and quality assurance, and includes informative discussions about essential quality parameters, primary focuses, and validation process.Moreover, the paper discusses potential testing methods for big data application systems.Furthermore, the primary issues, challenges, and needs in testing big data application are presented.

With the rapid advance of mobile computing technology and wireless networking, there is a significant increase of mobile subscriptions.This brings new business requirements and demands in mobile software testing, and causes new issues and challenges in mobile testing and automation.Current existing mobile application testing tools mostly concentrate on GUI, load and performance testing which seldom consider large-scale concurrent automation, coverage analysis, fault tolerance and usage of well-defined models.This paper introduces an implemented system that provides an automation solution across platforms on diverse devices using GUI ripping test scripting technique.Through incorporating open source technologies such as Appium and Selenium Grid, this paper addresses the scalable test automation control with the capability of fault tolerant.Additionally, maximum test coverage can also be obtained by executing parallel test scripts within the model.Finally, the paper reports case studies to indicate the feasibility and effectiveness of the proposed approach.

With the rapid advance of mobile computing, cloud computing and wireless network, there is a significant increase of mobile subscriptions. This brings new business requirements and demands in mobile testing service, and causes new issues and challenges. In this paper, informative discussions about cloud-based mobile testing-as-a-service (mobile TaaS) are offered, including the essential concepts, focuses, test process, and the expected testing infrastructures. Moreover, the paper presents a comparison among cloud-based mobile TaaS approaches and several best practices in industry are discussed. Futhermore, the primary issues, challenges, and needs are analyzed.

With the rapid advance of mobile computing technology and wireless networking, there is a significant increase of mobile applications (apps). This brings new business requirements and demands in mobile software testing, and causes new issues and challenges in mobile test automation. Existing mobile application testing approaches mostly concentrate on GUI-based testing, load and performance testing without considering large-scale concurrent mobile app test automation, and model-based test coverage analysis. In this paper, a mobile hierarchical GUI model is proposed to present mobile operation scenario flows and gesture features in a hierarchical manner, in order to facilitate test dependency analysis in test automation. Mobile app test coverage analysis is performed based on GUI ripping models. The paper also presents a developed system that provides a test automation solution using GUI models. Finally, the paper reports a case study to indicate the feasibility and effectiveness of the proposed approach.

Location-based services (LBS) are very important mobile app services, which provide diverse mobility services for mobile users anywhere and anytime.This brings new demands, issues, and challenges in mobile application testing.Today, mobile applications provide location-based service functions based on dynamic location contexts, mobile users and their travel patterns to deliver location-based mobile data, and service actions.Current software testing methods do not consider location-based validation coverage.Hence, there is a lack of research results addressing location-based mobile application testing.This paper focuses on mobile LBS testing.A novel test object model is proposed for quality validation of location-based mobile information services.In addition, the related test coverage metrics are also presented.These metrics can be useful for test engineers in designing test cases.A case study based on student testers is reported to demonstrate the potential application of the proposed model.

Green computing and energy-saving have been important and hot research topics in mobile computing and cloud computing. In the recent years, many existing research results have been published to address energy saving issues and challenges in cloud computing or mobile computing. Today, the significant increase of mobile users and strong business interests in cloud computing and services drive an emergent opportunities and needs in mobile cloud computing. This brings a new attention to energy saving in mobile cloud computing and services. This paper first presents the research scope and classified issues in energy saving in mobile clouds. Then, it reviews the existing research results and techniques, and examines their strengths and weaknesses. Finally, the paper offers observations, and identifies the open issues and needs for future research.

With the fast advancements in cloud computing and software- as-a-service (SaaS), testing and evaluation of cloud-based software and SaaS applications became an important task for engineers. Since most existing tools are not developed to support cloud-based software testing and SaaS evaluation, there is a strong demand for a new cloud-based testing infrastructure and evaluation environment for SaaS applications. This paper proposes a testing-as-service (TaaS) infrastructure and reports a cloud-based TaaS environment with tools (known as CTaaS) developed to meet the needs in SaaS testing, performance and scalability evaluation. The paper presents TaaS concepts and CTaaS, including their infrastructure, design and implementation. In addition, the paper demonstrates the application results of our previously proposed graphic models and metrics for SaaS performance and scalability evaluation. Moreover, the paper reports one case study for a selected SaaS (OrangeHRM) using the developed TaaS environment.

With the swift increase of the number of mobile device users, more wireless information services and mobile commerce applications are needed. Since various barcodes have been used for decades as a very effective means in many traditional commerce systems, today people are looking for innovative solutions to use barcodes in the wireless world. Recently, the mobile industry began to pay more attention to barcode applications in m-commerce because 2D-barcodes not only provide a simple and inexpensive method to present diverse commerce data, but also improve mobile user experience by reducing their inputs. This paper first discusses 2D-barcode concepts, types and classifications, major technology players, and applications in mobile commerce. Then, it reports a research project to develop a 2D-barcode processing solution to support mobile applications. Moreover, the paper also presents the application examples, and case study using the solution.

International Journal of Software Engineering and Knowledge EngineeringVol. 17, No. 03, pp. 311-313 (2007) Featured Topic: Testing and Quality Assurance for Component-Based Systems; Guest Editors: Jerry Gao and Sami BeydedaNo AccessGUEST EDITORS' INTRODUCTIONJERRY GAO and SAMI BEYDEDAJERRY GAOSan Jose State University, USA and SAMI BEYDEDAZIVIT, Bonn, Germanyhttps://doi.org/10.1142/S0218194007003288Cited by:0 Next AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out our titles in C++ Programming! FiguresReferencesRelatedDetails Recommended Vol. 17, No. 03 Metrics History PDF download

In component-based software engineering, software systems are mainly constructed based on reusable components, such as third-party components and in-house built components. Hence, system quality depends on the quality of the involved components. Any change of a component, it must be re-tested at the unit level, and re-integrated to form component-based application systems. Although a number of recently published papers address regression testing and maintenance of component-based systems, very few papers discuss how to identify component changes and impacts at the unit level, and find out the reusable test cases in a component's test suite to support its evolution. This paper focuses on component API-based changes and impacts, and proposes a systematic re-test method for software components based on a component API-based test model. The proposed method has been implemented in a component test tool, known as COMPTest. It can be used to automatically identify component-based API changes and impacts, as well as reusable test cases in a component test suite. The paper also reports this tool and its application results

Service-oriented architecture (SOA) is a system architecture in which a collection of loosely coupled services communicate with each other using standard interfaces and message-exchanging protocols. As an emerging technology in software development, the SOA presents a new paradigm, and it affects the entire software development cycle including analysis, specification, design, implementation, verification, validation, maintenance and evaluation. This paper proposes several testability evaluation criteria for SOA software, which serves as a reference for both service providers and application builders to evaluate the test support to SOA software. The proposed evaluation criteria are illustrated in a stock-trading case study

Constructing component-based software using reusable components is becoming a widely used approach. Since the quality of a component-based system is highly dependent on the quality of its components, component quality validation becomes very critical to both component vendors and users. Effectively validating component quality needs adequate test models and testing coverage criteria. This paper proposes an adequate test model and test coverage criteria for component validation. The paper discusses a dynamic approach to analyze component test coverage based on the proposed test model and test coverage criteria. The major contribution of this paper is its dynamic test coverage analysis solution to monitor API-based component validation and reuse. The paper reports the recent development efforts of a component test coverage analysis tool, and presents an application example.

Since components are the major building blocks for component-based systems, developing high quality components is becoming very critical for in component-based software engineering. To generate high quality components, we must pay attention to component testability to ensure that reusable components not only can be tested by component vendors, but also can be easily validated by component users. Therefore, component testability analysis, verification and measurement become very important research topic in testing components and component-based systems. This paper discusses the component testability in a quantifiable approach based on a component testability analysis model. Engineers can use this model to verify and measure component testability during a component development process. Based on this testability model, the paper discusses component testability verification, and proposes a pentagram model for testability measurement.

This paper presents a wireless-based virtual salesman system to interact with mobile users by providing an electronic virtual salesman to support wireless shopping. The paper not only reports the system architecture and design, but also introduces a virtual salesman concept and presents an intelligent solution which supports the interactions between virtual salesman and mobile users. A context-based interaction model is proposed as the base of the intelligent solution, and the related reasoning rules are given to model marketing and sales business logistics. The major difference between this system and the existing online-based e-commerce systems is that our system uses an electronic virtual salesman to actively interact with end customers to drive and conduct product promotions, selection, and trading operations. The paper presents an application example to demonstrate how to automatically interact with mobile device users using a wireless-based virtual salesman.

The World Wide Web (WWW) technology has been widely accepted and used to build different application systems on the Internet and/or Intranet due to its advantages in networking, platform independence, distributed accesses for multiple users at different locations. We believe that this new technology is a powerful and cost effective tool to build a next generation environment for supporting software engineering practice in a software product's life cycle. The paper reports our experience on developing a Web based testing information sharing, control and management system using WWW technology. The paper also discusses its approach, basic function features, related design and implementation mechanisms. In addition, it reports the first hand useful experience and lessons in developing Web based software engineering support systems.

Object state behavior implies that the effect of an operation on an object may depend on the states of the object and other objects. It may cause state changes to more than one object. Thus, the combined or composite effects of the object operations must be analyzed and tested. We show that certain object state behavior errors cannot be detected readily by conventional testing methods. We describe an object state test method consisting of an object state model, a reverse engineering tool, and a composite object state testing tool. The object state test model is an aggregation of hierarchical, concurrent, communicating state machines envisioned mainly for object state testing. The reverse engineering tool produces an object state model from any C++ program. The composite object state testing tool analyzes the object state behaviors and generates test cases for testing object state interactions. We show the detection of several composite object state behavior errors that exist in a well-known thermostat example.

Since polymorphism in object-orientated (OO) programming is an important feature and tool to increase the reusability and extensibility of object-orientated programs, understanding and identifying polymorphism changes and their impacts in an object-orientated program is very important in software maintenance. Although many published research articles have addressed the polymorphism feature in object-orientated programs, almost none of them have discussed software maintenance issues on identification of polymorphism change and impact. This paper proposes a formal software maintenance model for polymorphism in object-orientated programs, and discusses a systematic approach to track different polymorphism changes and their ripple effects. In addition, it reports on the implementation of a relevant tool, OOTME, and its use on two case studies.

Since class objects are the major components in object oriented (OO) programs during run time, testing the behavior of these objects become an important task in testing of object oriented programs to complement other types of testing, such as, structure testing and functional testing. To check the behavior of objects in OO programs, it is essential to check each state and each transition for every object. The paper proposes a new test model, called object state diagram, to capture and represent the dynamic behavior of objects in an OO program. The model is an extension of the existing state models, it supports basic OO features, such as class objects, inheritance, aggregation and overloading. Unlike other models, it is a concurrent, communicating state machine which has the capability of representing the object state hierarchy for a complex class object. Based on this model we address the issues and solutions to object state test strategy, test criteria and test generation.

The complexity and interdependencies of an object oriented program makes testing of such programs difficult. We present a reverse engineering based model called Object Relation Diagram (ORD), which is generated from analyzing the C++ source code of an object oriented program. An ORD is a directed graph in which the vertices represent the object classes and the edges represent the relationships among the object classes. Based on the ORD, a test strategy, called test order, for unit testing and integration testing of object oriented programs is described. The test order algorithm uses topological sorting and clusters of strongly connected subgraphs of the ORD. It computes the optimal test order in the sense that the effort required to construct the test stubs to simulate the untested classes/member functions is minimum. We show the savings of the test strategy through statistics of the InterViews library.

Acceptance testing is the determining factor in the satisfaction of the contract between the software vendor and the customer. In today's industry practice, there is a lack of a systematic method to help testers (or customers) construct, formalize, and verify acceptance testing models, and use them for automatic test case generation. This paper proposes a systematic approach to form the acceptance test model for a software system to represent its external behavior from the users' perspectives. Based on the generated test model, different rigorous acceptance test criteria are defined, and various types of acceptance testing are discussed.>

The importance of object state testing is illustrated through a simple example. We show that certain errors in the implementation of object state behavior cannot be readily detected by conventional structural testing, functional testing, and state testing. We describe an object state test model and outline a reverse engineering method for extracting object state behaviors from C++ source code. The object state test model is a hierarchical, concurrent, communicating state machine. It resembles the concepts of inheritance and aggregation in the object-oriented paradigm rather than the concept of state decomposition as in some existing models. The reverse engineering method is based on symbolic execution to extract the states and effects of the member functions. The symbolic execution results are used to construct the state machines. The usefulness of the model and of the method is discussed in the context of object state testing in the detection of a state behavior error. >

In the object-oriented (OO) paradigm, new features (such as encapsulation, aggregation, inheritance, polymorphism and dynamic binding) introduce new problems in software testing and maintenance. One of them is the difficulty of identifying the affected components (such as classes) when changes are made in object-oriented class libraries or programs. This paper discusses the types of code changes in an object-oriented class library, and provides an automated solution to identify different kinds of code changes and their impact. In addition, an OO software maintenance environment that implements the research result is described. Our experience with the environment prototype shows promising results.>