In this paper, we address the problem of recognizing the semantic human activities through the analysis of large dataset collected from users’ sensor-based smartphones. Our approach is unique in terms of covering a large number of activities that users could possibly engage in, and considering the multi-level-based classification model. Our model has three properties that never seemed to be addressed

Influence maximization is a problem that aims to select top k influential nodes to maximize the spread of influence in social networks. The classical greedy-based algorithms and their improvements are relatively slow or not scalable. The efficiency of heuristic algorithms is fast but their accuracy is unacceptable. Some algorithms improve the accuracy and efficiency by consuming a large amount of memory

Topic word extraction is the task of identifying single or multi-word expressions that represent the main topics of a document. In this paper, two improved algorithms for extracting and discovering topic words are proposed in the Rapid Topic word Detection (RTD) Algorithm and CategoryTextRank (CTextRank) Algorithm, which can effectively obtain information by extracting and filtering the topic words

Mutation testing aims to evaluate the fault detection capability of a test suite. This evaluation substitutes faults with mutants by transforming program code to be defective. Evidences of the relationship between the detection rates of mutants and real faults have supported the use of mutants. It has also been known that the test suite size was a significant factor affecting the relationship. Our

For the test case prioritization problems, the average percent of faults detected (APFD) and its variant versions are widely used as metrics to evaluate prioritized test suite’s efficiency of fault detection. By a revisit of metrics for test case prioritization, we observe that APFD is only available for the scenarios where all test suites under evaluation contain the same number of test cases. Such

Fixed Sized Candidate Set (FSCS) is the first of a series of methods proposed to enhance the effectiveness of random testing (RT) referred to as Adaptive Random Testing methods or ARTs. Since its inception, test case generation overheads have been a major drawback to the success of ART. In FSCS, the bulk of this cost is embedded in distance computations between a set of randomly generated candidate

Train delay is a serious issue that can spread rapidly in the railway network leading to further delay of other trains and detention of passengers in stations. However, the current practice in the event of the trail delay usually depends on train dispatcher’s experience, which cannot manage train operation effectively and may have safety risks. The application of intelligent railway monitor and control

Accurate prediction of train delay recovery is critical for railway incident management and providing passengers with accurate journey time. In this paper, a two-stage prediction model is proposed to predict the recovery time of train primary-delay based on the real records from High-Speed Railway (HSR). In Stage 1, two models are built to study the influence of feature space and model framework on

The catenary support device inspection is of crucial importance for ensuring safety and reliability of railway systems. At present, visual detection tasks of catenary support devices defect are performed by trained personnel based on the images taken periodically by industrial cameras installed on inspection vehicle in a limited period of time at midnight. However, the inspection mean is inappropriate

In China, the turnout abnormality very easily causes traffic accident or affects the efficiency due to the operating environment of railway transportation. The existing monitoring means are relatively backward, and mature automatic diagnosis method is lacking. In this study, a method based on semi-supervised learning algorithm for abnormal state diagnosis of turnout action curve is proposed, which

The deployment change of SaaS (Software as a Service) software will influence its response time, which is an important performance metric. Therefore, studying the impact of deployment change on the response time of SaaS software could contribute to performance improvement of the software. However, there are few performance analysis methods which can directly analyze the relationship between deployment

Trace links between requirements and software artifacts provide available traceability information and in-depth insights for different stakeholders. Unfortunately, establishing requirements trace links is a tedious, labor-intensive and fallible task. To alleviate this problem, Information Retrieval (IR) methods, such as Vector Space Model (VSM), Latent Semantic Indexing (LSI), and their variants, have

The architecture smells are the poor design practices applied to the software architecture design. The smells in software architecture design can be cascaded to cause the issues in the system implementation and significantly affect the maintainability and reliability attribute of the software system. The prevention of architecture smells at the design phase can therefore improve the overall quality

Understanding user’s search intent in vertical websites like IT service crowdsourcing platform relies heavily on domain knowledge. Meanwhile, searching for services accurately on crowdsourcing platforms is still difficult, because these platforms do not contain enough information to support high-performance search. To solve these problems, we build and leverage a knowledge graph named ITServiceKG to

In the last decade, REST has become the most common approach to provide web services, yet it was not originally designed to handle typical modern applications (e.g. mobile apps). GraphQL was proposed to reduce the number of queries and data exchanged in comparison with REST. Since its release in 2015, it has gained momentum as an alternative approach to REST. However, generating and maintaining GraphQL

In recent years, new classes of highly dynamic, complex systems are gaining momentum. These classes include, but are not limited to IoT, smart cities, cyber-physical systems and sensor networks. These systems are characterized by the need to express behaviors driven by external and/or internal changes, i.e. they are reactive and context-aware. A desirable design feature of these systems is the ability

Existing Web API search engines allow only category-based browsing and keyword- or tag-based searches for RESTful services. In other words, they do not enable the discovery or composition of real-world RESTful services by application developers. This paper outlines a novel scheme, called Transformation–Annotation–Discovery (TAD), which transforms OpenAPI (Swagger) documents related to RESTful services

Considerable research has recently focused on integrating cyber-physical systems in a social context. However, several challenges remain concerning appropriate methodologies, frameworks and techniques for supporting socio-cyber-physical collaboration. Existing systems do not recognize how cyber-physical resources can be socially connected so that they interact in collaborative decision-making like

Software maintenance is the task of modifying a running product previously delivered to the client, in order to correct defects, improve performance or adapt it to the environment. This task is a crucial activity for enterprises. Without it, existing systems would become rapidly out-of-date and inefficient. The purpose of this paper is to present a software maintenance approach used in small and medium-sized

Most clustering algorithms build disjoint clusters. However, clusters might be overlapped because documents may belong to two or more categories in the real world. For example, a paper discussing the Apple Watch may be categorized into either 3C, Fashion, or even Clothing and Shoes. Therefore, overlapping clustering algorithms have been studied such that a resource can be assigned to one or more clusters

In recent years, deep learning models have shown great potential in source code modeling and analysis. Generally, deep learning-based approaches are problem-specific and data-hungry. A challenging issue of these approaches is that they require training from scratch for a different related problem. In this work, we propose a transfer learning-based approach that significantly improves the performance

Automated and reliable software verification is of crucial importance for development of high-quality software. Formal methods can be used for finding different kinds of bugs without executing the software, for example, for finding possible run-time errors. The methods like model checking and symbolic execution offer very precise static analysis but on real world programs do not always scale well.

Because of the sequence and number of calls of functions, software network cannot reflect the real execution of software. Thus, to detect crucial functions (DCF) based on software network is controversial. To address this issue, from the viewpoint of software dynamic execution, a novel approach to DCF is proposed in this paper. It firstly models, the dynamic execution process as an execution sequence

Logs play an important role in the maintenance of large-scale systems. The number of logs which indicate normal (normal logs) differs greatly from the number of logs that indicate anomalies (abnormal logs), and the two types of logs have certain differences. To automatically obtain faults by K-Nearest Neighbor (KNN) algorithm, an outlier detection method with high accuracy, is an effective way to detect

Nowadays, one of the biggest challenges in banking sector, certainly, is assessment of the client’s creditworthiness. In order to improve the decision-making process and risk management, banks resort to using data mining techniques for hidden patterns recognition within a wide data. The main objective of this study is to build a high-performance customized credit scoring model. The model named Reliable

With the aggrandizement scale of software system, the number of test cases has grown explosively. Test case prioritization (TCP) has been widely used in software testing to effectively improve testing efficiency. However, traditional TCP methods are mostly based on software code and they are difficult to apply to model-based testing. Moreover, existing model-based TCP techniques often do not take the

In this paper, we introduce a framework for knowledge reuse and deduction in mechanical products design and development. The proposed system effectively exploits the capitalized and inferred knowledge. To this end, we settled up an ontology dealing with the design process of mechanical products such as “the car”. The ontology-based framework is supported by a software tool that brings an automatic

Extracting the usability requirements are crucial during requirements review and requirements validation for different purposes. Usability requirements are hard to be determined until the real user has experienced the software. It is even more challenging when these usability requirements are documented in natural language, which has an inconsistent and unrestricted structure. Automated requirements

For software testing, it is well known that the architecture of a software system can be utilized to enhance testability, fault detection and error locating. However, how much and what effects architecture-based software testing has on software testing have been rarely studied. Thus, this paper undertakes case study investigation of the effects of architecture-based software testing specifically with

Software cost estimation is one of the most crucial tasks in a software development life cycle. Some well-proven methods and techniques have been developed for effort estimation in case of classical software. Mobile applications (apps) are different from conventional software by their nature, size and operational environment; therefore, the established estimation models for traditional desktop or web

Until now, numerous effort estimation models for software projects have been developed, most of them producing accurate results but not providing the flexibility to decision makers during the software development process. The main objective of this study is to objectively and accurately estimate the effort when using the Scrum methodology. A dynamic effort estimation model is developed by using regression-based

Case-based reasoning heavily depends on the structure and content of the cases, and semantics is essential to effectively represent cases. In the field of structured case representation, most of the works regarding case representation and measurement of semantic similarity between cases are based on model-theoretic semantics and their extensions. The purpose of this study is to explore the potential

Users frequently raise feedback when using software products. Feedback from users regarding their experiences and expectations and software defects they found adds values to software maintenance and evolution — software managers collect user feedback and then dispatch feedback issues that developers (and/or maintainers) need to track and process. Feedback tracking is often supported by open source

Software sustainability evaluation has become an essential component of software engineering (SE) owing to sustainability considerations that must be incorporated into software development. Several studies have been performed to address the issues associated with sustainability concerns in the SE process. However, current practices extensively rely on participant experiences to evaluate sustainability

Testing multi-thread programs becomes extremely difficult because thread interleavings are uncertain, which may cause a program getting different results in each execution. Thus, Thread Scheduling Sequence (TSS) is a crucial factor in multi-thread program testing. A good TSS can obtain better testing efficiency and save the testing cost especially with the increase of thread numbers. Focusing on the

We propose a simple, fast, deterministic pre-fitting approach which derives the Baum–Welch algorithm initial values directly from the input data. Such pre-fitting has the purpose of improving the fitting time for a given Hidden Markov Model (HMM) while maintaining the original Baum–Welch algorithm as the fitting one. The fitting time is improved by avoiding the Baum–Welch algorithm sensitiveness through

Context: Companies must make a paradigm shift in which both short- and long-term value aspects are employed to guide their decision-making. Such need is pressing in innovative industries, such as ICT, and is the core of Value-based Software Engineering (VBSE). Objective: This paper details three case studies where value estimation models using Bayesian Network (BN) were built and validated. These estimation

By suggesting new visiting places, point-of-interest (POI) recommendation not only assists users to find their preferred places, but also helps businesses to attract potential customers. Recent studies have proposed many approaches to the POI recommendation. However, the data sparsity and complexity of user check-in behavior still pose big challenges to accurate personalized POI recommendation. To

In last few years, applying language model to source code is the state-of-the-art method for solving the problem of code completion. However, compared with natural language, code has more obvious repetition characteristics. For example, a variable can be used many times in the following code. Variables in source code have a high chance to be repetitive. Cloned code and templates, also have the property

This paper proposes an architecture for sharing IoT Objects’ resources in the Internet of Things providing a model for its owners to expose devices, which can be consumed by clients inspired by the Sensor-as-a-Service model. The main idea relies on the fact that users, such as developers and researchers, do not always have access to the necessary hardware and resources. Exposing devices in IoT should

Intelligent computing techniques have a paramount importance to the treatment of cybersecurity incidents. In such Artificial Intelligence (AI) context, while most of the algorithms explored in the cybersecurity domain aim to present solutions to intrusion detection problems, these algorithms seldom approach the correction procedures that are explored in the resolution of cybersecurity incident problems

Since Correlation Filter appeared in the field of video object tracking, it is very popular due to its excellent performance. The Correlation Filter-based tracking algorithms are very competitive in terms of accuracy and speed as well as robustness. However, there are still some fields for improvement in the Correlation Filter-based tracking algorithms. First, during the training of the classifier

Libraries offer reusable functionality through Application Programming Interfaces (APIs) with usage constraints such as call conditions or orders. Constraint violations, i.e. API misuses, commonly lead to bugs and security issues. Although researchers have developed various API misuse detectors in the past few decades, recent studies show that API misuse is prevalent in real-world projects, especially

Cloud computing is an emerging computing paradigm in IT industries. The wide adoption of cloud computing is raising concerns about management of data in the cloud. Access control and data security are two critical issues of cloud computing. Time-efficient secure access control (TESAC) model is a new data access control scheme which can minimize many significant problems. This scheme has better performance

Multistep flow prediction is an essential task for the car-sharing systems. An accurate flow prediction model can help system operators to pre-allocate the cars to meet the demand of users. However, this task is challenging due to the complex spatial and temporal relations among stations. Existing works only considered temporal relations (e.g. using LSTM) or spatial relations (e.g. using CNN) independently

Background: The use of chatbots has increased considerably in recent years. These are used in different areas and by a wide variety of users. Due to this fact, it is essential to incorporate usability in their development. Aim: Our objective is to identify the state-of-the-art in chatbot usability and applied human–computer interaction techniques, to analyze how to evaluate chatbot usability. Method:

Counterexample-guided abstraction refinement (CEGAR) is an extremely successful methodology for combating the state-space explosion problem in model checking. State-space explosion problem is more serious in the field of stochastic model checking, and it is still a challengeable problem to apply CEGAR in stochastic model checking effectively. In this paper, we formalize the problem of applying CEGAR

Attaining effective communication within and across organizational units is among the most critical challenges for success in software development organizations. This paper presents a novel model, supporting analysis of problems in inter-departmental communication events. The model was developed and designed based on industrial needs emphasizing flexibility, applicability and scalability. The model

Agile methods have provided significant contributions to Software Engineering. This work presents a new process for Software Requirements Specification, integrating Agile Properties and regulated environments, such as aviation, medical, nuclear and automotive, among others. The Software in Regulated Environments (SRE) involves plan-driven methods with needed documentation to ensure safety, reliability

Web API recommendations have recently been studied extensively. However, recommending an API for a service is different than service intelligence. Web API automatic calls are widely used in question–answer dialog applications and service-composed workflow systems to achieve intelligent services. To finish an automatic Web API call not only requires the Web API ID, but also its input parameters. In

Inadequate documentation of software design has been known to be a barrier for developers. Interestingly, several relevant object-oriented systems have their design documented using key classes, which are meant to represent key concepts of the systems. In order to fill the gap of under-documented design, we present Keecle, an approach for detecting a predefined number of key classes in a semi-automatic

There have been concerting policy and legal initiatives to mitigate the privacy harm resulting from badly designed software technology. But one main challenge to realizing these initiatives is the difficulty in translating proposed principles and regulations into concrete and verifiable evidence in technology. This is partly due to the lack of systematic techniques and tools to address privacy in the

The existing techniques for software testing can be used to perform only a particular type of testing, and moreover proficiency is required to write the automation test scripts using these techniques. This paper proposes a novel software testing approach using Aspect-Oriented Programming (AOP) that alone suffices for carrying out most of the types of software testing and thus obliterates the need of

This paper improves the breadth-first search strategy in directed automated random testing (DART) to generate a fewer number of test data while gaining higher branch coverage, namely Static DART or SDART for short. In addition, the paper extends the test data compilation mechanism in DART, which currently only supports the projects written in C, to generate test data for C++ projects. The main idea

Data logging is helpful for the operation and maintenance manager of SaaS-based solutions to diagnose performance issues. However, long-running SaaS software may generate huge amounts of log data which is difficult to analyze, and it lacks a systematic approach to collect the running log and lacks a unified data structure to normalize the performance-related data. All these threaten the timeliness

This paper proposes a transformation-based method to automatically generate functional test cases from use cases named USLTG (Use case Specification Language (USL)-based Test Generation). We first focus on developing a modeling language named Test Case Specification Language (TCSL) in order to express test cases. Test cases in TCSL can contain detailed information including test steps, test objects

An effective method for addressing the configuration optimization problem (COP) in Software Product Lines (SPLs) is to deploy a multi-objective evolutionary algorithm, for example, the state-of-the-art SATIBEA. In this paper, an improved hybrid algorithm, called SATIBEA-LSSF, is proposed to further improve the algorithm performance of SATIBEA, which is composed of a multi-children generating strategy

Software defect prediction technology has been widely used in improving the quality of software system. Most real software defect datasets tend to have fewer defective modules than defective-free modules. Highly class-imbalanced data typically make accurate predictions difficult. The imbalanced nature of software defect datasets makes the prediction model classifying a defective module as a defective-free

Model transformation is a keystone in carrying out model-driven development. Currently, many studies have focused on model transformation, those researches study the automatic transformation from PIM to PSM and from PSM to code, but only few have dealt with how the CIM-level model is automatically mapped onto the PIM-level model. Even though some studies have proposed the semi-automatic transformation

The semiconductor manufacturing process is very complex, and it is the most important part of the semiconductor industry. In order to test whether or not wafers are functioning normally, a pass/fail test is conducted; however, time and cost needed for this testing increase as the number of chips increases. To address this, a machine learning technique is adopted and a high-performance classifier is