理學 : The science

In the modern digital era, software systems are extensively adapted and have become an integral component of human society. Such wide use of software systems consists of large and more critical data that inevitably needs to be secured. It is imperative to make sure that these software systems not only satisfy the users’ needs or functional requirements, but it is equally important to make sure the security of these software systems. However, recent research shows that many software development methods do not explicitly include software security measures during software development as they move from demand engineering to their final losses. Integrating software security at each stage of the software development life cycle (SDLC) has become an urgent need. Tackling software security, various methods, techniques, and models have been suggested and developed, however, only a few of them provide strong evidence for building secure software applications. The main purpose of this research is to study security measures in the context of the development of secure software (SSD) during the study of systematic mapping (SMS). Based on the inclusion and exclusion criteria, 116 studies were selected. After the data extraction from the selected 116 papers, these were classified based on the quality assessment, software security method, SDLC phases, publication venue, and SWOT analysis. The results indicate that this domain is still immature and sufficient research work needs to be carried out particularly on empirically evaluated solutions.

Systematic Mapping Study on Security Approaches in Secure Software Engineering

With the continuous development of agricultural mechanization and information, agricultural machinery failure analysis has become an important issue. Providing effective agricultural machinery failure analysis for agricultural workers can save considerable time and cost. This paper addresses this issue by designing and producing a platform for data analysis of agricultural engineering machinery. Through the collection and analysis of agricultural engineering machinery floor data and with the help of the Internet and big data technology, physical modeling, charts, and other intuitive forms have been developed to provide information for personnel on the mechanical parts of agricultural engineering machinery, the cause of failures, and maintenance requirements. This novel agricultural engineering machinery management analysis platform currently has access to 100 million data points, with system access for capacity expansion, and provides fault detection and maintenance examples. It has already been applied in multiple farms across many of China's provinces.

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Fault Analysis System for Agricultural Machinery Based on Big Data

The validation of a cloud system can be complicated by the size of the system, the number of users that can concurrently request services, and the virtualization used to give the illusion of using dedicated machines. Unfortunately, it is not feasible to use conventional testing methods with cloud systems. This article proposes a framework, called TEA- Cloud , that integrates simulation with testing methods for validating cloud system designs. Testing is applied on both functional and nonfunctional aspects of the cloud, like performance and cost. The aim of the framework is to provide a complete methodology to help users to model both software and hardware parts of cloud systems and automatically test the validity of these clouds using a cost-effective approach. Metamorphic testing is used to overcome the lack of an oracle that checks whether the behavior observed in testing is allowed. Metamorphic testing is based on metamorphic relations (MRs). We define three families of MRs, which target issues such as performance, resource provisioning, and cost. TEA- Cloud was evaluated through an empirical study that used fault seeding (mutation) and ten MRs for testing different cloud configurations. The results were promising, with TEA- Cloud finding all seeded faults.

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TEA- Cloud : A Formal Framework for Testing Cloud Computing Systems

With the rapid advance of big data and cloud computing, building high quality big data systems in different application fields has gradually became a popular research topic in academia and industry as well as government agencies. However, more quality problems lead to application errors. Although the current research work has discussed how to ensure the quality of big data applications from several aspects, there is no systematic discussion on how to ensure the quality of large data applications. Therefore, a systematic study on big data application quality assurance is very necessary and critical. This paper focuses on the survey of quality assurance techniques of big data applications, and it introduces big data properties and quality attributes. It mainly discusses the key approaches to ensure the quality of big data applications and they are testing, model-driven architecture (MDA), monitoring, fault tolerance, verification and also prediction techniques. In addition, this paper also discusses the impact of big data characteristics on big data applications.

A Survey on Quality Assurance Techniques for Big Data Applications

Web application testing is a great challenge due to the management of complex asynchronous communications, the concurrency between the clients-servers, and the heterogeneity of resources employed. It is difficult to ensure that a test case is re-running in the same conditions because it can be executed in undesirable ways according to several environmental factors that are not easy to fine-grain control such as network bottlenecks, memory issues or screen resolution. These environmental factors can cause flakiness, which occurs when the same test case sometimes obtains one test outcome and other times another outcome in the same application due to the execution of environmental factors. The tester usually stops relying on flaky test cases because their outcome varies during the re-executions. To fix and reduce the flakiness it is very important to locate and understand which environmental factors cause the flakiness. This paper is focused on the localization of the root cause of flakiness in web applications based on the characterization of the different environmental factors that are not controlled during testing. The root cause of flakiness is located by means of spectrum-based localization techniques that analyse the test execution under different combinations of the environmental factors that can trigger the flakiness. This technique is evaluated with an educational web platform called FullTeaching. As a result, our technique was able to locate automatically the root cause of flakiness and provide enough information to both understand it and fix it.

FlakyLoc: flakiness localization for reliable test suites in web applications

MapReduce is a parallel data processing paradigm oriented to process large volumes of information in data-intensive applications, such as Big Data environments. A characteristic of these applications is that they can have different data sources and data formats. For these reasons, the inputs could contain some poor quality data that could produce a failure if the program functionality does not handle properly the variety of input data. The output of these programs is obtained from a number of input transformations that represent the program logic. This paper proposes the testing technique called MRFlow that is based on data flow test criteria and oriented to transformations analysis between the input and the output in order to detect defects in MapReduce programs. MRFlow is applied over some MapReduce programs and detects several defects.

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Testing data transformations in MapReduce programs

New processing models are being adopted in Big Data engineering to overcome the limitations of traditional technology. Among them, MapReduce stands out by allowing for the processing of large volumes of data over a distributed infrastructure that can change during runtime. The developer only designs the functionality of the program and its execution is managed by a distributed system. As a consequence, a program can behave differently at each execution because it is automatically adapted to the resources available at each moment. Therefore, when the program has a design fault, this could be revealed in some executions and masked in others. However, during testing, these faults are usually masked because the test infrastructure is stable, and they are only revealed in production because the environment is more aggressive with infrastructure failures, among other reasons. This paper proposes new testing techniques that aimed to detect these design faults by simulating different infrastructure configurations. The testing techniques generate a representative set of infrastructure configurations that as whole are more likely to reveal failures using random testing, and partition testing together with combinatorial testing. The techniques are automated by using a test execution engine called MRTest that is able to detect these faults using only the test input data, regardless of the expected output. Our empirical evaluation shows that MRTest can automatically detect these design faults within a reasonable time.

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Automatic Testing of Design Faults in MapReduce Applications

MapReduce is a paradigm that allows parallel processing of large amounts of data. MapReduce programs combined with their underlying run-time framework have distinctive features that are prone to include unexpected behaviors not present in other types of programs. This paper describes an approach to functional testing of MapReduce programs based on a hierarchical classification of a number of potential faults that may occur in MapReduce programs over Hadoop. This classification, called MRTree, is then used to derive test cases able to detect the faults represented in MRTree and illustrated with some examples.

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MRTree: Functional Testing Based on MapReduce's Execution Behaviour

Trabajo apoyado en parte por los proyectos TIN2016‐76956 ‐ C3‐1 ‐ R y TIN2013‐46928 ‐ C3‐1, financiado por el Ministerio de Economia y Competitividad de Espana, y GRUPIN14‐007, financiado por el Principado de Asturias (Espana) y FEDER

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Jesus Moran

Papers

FlakyLoc: flakiness localization for reliable test suites in web applications

Testing data transformations in MapReduce programs

Automatic Testing of Design Faults in MapReduce Applications

MRTree: Functional Testing Based on MapReduce's Execution Behaviour

Testing MapReduce programs: A systematic mapping study