Eclipse IDE Research Articles

Code Generator Development to Transform IFML (Interaction Flow Modelling Language) into a React-based User Interface

Model-Driven Software Engineering (MDSE) is a software development approach that uses the Model to be the main actor of the development. MDSE can be applied to User Interface (UI) Development so that a model for the UI can be built, and then a transformation can be made to turn it into a running application. In this research, we develop UI Generator to support UI Development with the MDSE approach. This UI Generator can also support UI Development in Software Product Line Engineering (SPLE) paradigm. The UI is modeled with Interaction Flow Modeling Language (IFML) diagram. Then The IFML diagram is transformed into React-Based UI by the UI Generator. The UI Generator is developed with Acceleo on Eclipse IDE to transform IFML into React Code with the transformation rules defined in this research. The UI generator is also enriched with display settings and static page management to address user customization needs. The experimental results show that the UI Generator can generate a functional website. Besides evaluating the working product, UI Generator is evaluated qualitatively well based on six quality criteria as an SPLE supporting tool.

Implementation of Model Based Testing for Testing Kawn Subscriptions Manager Application

This research focuses on the Kawn Subscriptions Manager app, which was developed to help Food and Beverage (F&B) businesses manage customer subscriptions. Currently, the Kawn Subscriptions Manager application does not have a standardized test suite that has been validated. Therefore, in this research, Model-Based Testing (MBT) is applied in testing the Kawn Subscriptions Manager application as a testing method to produce a specific and technical test suite. MBT is a testing method based on the behavioral model of the system being tested. Then, to measure the adequacy of test cases generated by MBT, mutation testing is used. The model is created using the GraphWalker tool and testing is done automatically using Selenium and testNG in the Eclipse IDE text editor. From the results of the study, it is known that MBT testing generates test cases from the behavior model of the function being tested. Testing with MBT shows that no failures were found in the tested functions. Then the average mutation score is 72.18% which indicates that the test cases generated by MBT are adequate or in the medium category. However, it is important to note that while MBT was successful in generating test cases that examined the application's behavior, it was found that the method was unable to detect failures caused by mutants that did not significantly affect the application's behavior. This finding suggests that while MBT can be an effective tool for testing key functions, additional methods are needed to identify failures that may result from minor or insignificant changes to the application code.

An empirical evaluation of the formative feedback supported by dashboard in the context of compilation error

AbstractFormative feedback is one of the most recognized types of feedback in academics. However, for feedback to be effective it must be task‐specific, immediate, corrective and positive. At present, automatic feedback is far from these characteristics and leaves the instructor as a fundamental agent in the teaching‐learning process of programming. Faced with this scenario, the lecturer needs support tools that help them to give formative feedback in educational scenarios with many students and little time. In this paper, we have demonstrated that, by using a tool called COLMENA, any lecturer may give students effective formative feedback which is task‐specific, immediate and corrective. COLMENA is a system that combines real‐time Eclipse IDE data retrieval on compilation errors and analytical dashboards in a webapp solution for lecturers and students. In this study, two different approaches have been compared, considering two academic courses with and without COLMENA feedback, respectively. The results indicate that novice students receiving feedback from the lecturer via COLMENA reduced their errors, demonstrating that feedback generated from compilation errors is effective.

Tool Support for Improving Software Quality in Machine Learning Programs

Machine learning (ML) techniques discover knowledge from large amounts of data. Modeling in ML is becoming essential to software systems in practice. The accuracy and efficiency of ML models have been focused on ML research communities, while there is less attention on validating the qualities of ML models. Validating ML applications is a challenging and time-consuming process for developers since prediction accuracy heavily relies on generated models. ML applications are written by relatively more data-driven programming based on the black box of ML frameworks. All of the datasets and the ML application need to be individually investigated. Thus, the ML validation tasks take a lot of time and effort. To address this limitation, we present a novel quality validation technique that increases the reliability for ML models and applications, called MLVal. Our approach helps developers inspect the training data and the generated features for the ML model. A data validation technique is important and beneficial to software quality since the quality of the input data affects speed and accuracy for training and inference. Inspired by software debugging/validation for reproducing the potential reported bugs, MLVal takes as input an ML application and its training datasets to build the ML models, helping ML application developers easily reproduce and understand anomalies in the ML application. We have implemented an Eclipse plugin for MLVal that allows developers to validate the prediction behavior of their ML applications, the ML model, and the training data on the Eclipse IDE. In our evaluation, we used 23,500 documents in the bioengineering research domain. We assessed the ability of the MLVal validation technique to effectively help ML application developers: (1) investigate the connection between the produced features and the labels in the training model, and (2) detect errors early to secure the quality of models from better data. Our approach reduces the cost of engineering efforts to validate problems, improving data-centric workflows of the ML application development.

Two-Way Approach for Improved Real-Time Transmission in Fog-IoT-Based Health Monitoring System for Critical Patients

Health monitoring systems are now required, particularly for essential patients, following the COVID-19 pandemic, which was followed by its variants and other epidemics of a similar nature. Effective procedures and strategies are required, though, to react promptly to the enormous volume of real-time data offered by monitoring equipment. Although fog-based designs for IoT health systems typically result in enhanced services, they also give rise to issues that need to be resolved. In this paper, we propose a two-way strategy to reduce network latency and use while increasing real-time data transmission of device gateways used for sensors by making educated judgments for connection setup with BS and task assignment. For this, a simulation using iFogSim in the Eclipse IDE showed how effective the suggested strategy for massive IoT health monitoring systems is. The algorithm is analyzed for network usage and latency, and the results reveal 20%–25% improvements compared to the existing methods regarding network usage and latency.

An efficient web service annotation for domain classification and information retrieval systems using HADLNN classifier

Aimed at exchanging the dissimilar data between distributed applications, web services (WSs) annotations have progressed as a versatile and cost-effectual solution. Information retrieval (IR)assists in establishing the user-essential related information's searching impacts. However, rendering quick and efficient IR is a challenging problem. Also, the existent system yields slow accuracy, and as well the training time is high. Aimed at overcoming these problems, implemented an effective WS annotation aimed at domain classification and IR systems utilizing the Hybrid Artificial Deep Learning Neural Network (HADLNN). Firstly, the Semantic annotation (SA)stage is executed that comprises text preprocessing, repetitive data removal, feature extraction, and as well Ontology Construction. The text preprocessing offers the partitioning, stop word removal, and as well the stemming procedure aimed at the WSDL dataset. Next, continual WSs utilizing Hadoop Distributed File System (HDFS)is eliminated. After that, the CFC, confidence, support, and as well entropy attributes are taken out;next, the (Web Ontology Language) OWL files as of ontology construction are generated utilizing the protégé tool. After producing the OWL, the owl file is visualized utilizing Eclipse IDE and extracted the values utilizing the reasoner in the protege tool. After WS annotations, the domain is categorized centered on the connecting of WSs utilizing HADLNN. Lastly, the IR procedure is executed on MK-means that groups identical services as of the categorized domain. Preliminary outcomes exhibit that the system proposed offers efficient performance analogized to the existent techniques.

Threshold multiparty multi‐randomness secure partially homomorphic encryption for data security in cloud

AbstractThreshold cryptography is used to secure a cryptographic secret by dividing it into N shares and storing each claim on a diverse server. Any subgroup of t servers can use the secret without re‐making it. However, an adversary cannot recover the secret by compromising t − 1 servers. Homomorphic encryption operates directly on the ciphertext. In this study, a Threshold multiparty multi‐randomness secure partially homomorphic encryption (TM‐MR‐SPHE) algorithm is proposed to secure the outsourced data and perform multiplication and division operations on the ciphertext. The model is implemented in Eclipse IDE and AWS Toolkit for Eclipse and deployed in Amazon Elastic Beanstalk (EB) environment. This model is mainly used to secure the patient e‐health details and perform computation on outsourced data. The patient details are encrypted by the algorithm MR‐SPHE and uploaded in AWS (Amazon Web Service) S3 bucket. AWS identity and access management (IAM) creates the users' service and secret shares among multiple users in the EB environment. The proposed model performance is studied by comparing with other partially homomorphic Elgamal, Pailler, and Benaloh. This model achieves data integrity and confidentiality using the threshold multiparty with multi‐randomness secure partially homomorphic encryption.

QR based Penalty Charging for Traffic Police

The "QR BASEDPENALTY CHARGING FOR TRAFFIC POLICE" project was created utilising the Android 4.0.3 Standard platform. The platform used to develop the application is Eclipse IDE (Mars) with Java 1.7 Standard Edition. The project's focus is on using mobile apps to manage information about traffic infringement penalties. Through this software, the project processes accident information as well as licence details, car details, penalty details, and accident information. The project will also concentrate on a mobile app that will aid in learning information on the fines associated with traffic infractions as tracked by Vehicle No. on the road. Due to its quick readability and larger storage capacity compared to normal barcodes, the Quick Response (Vehicle No) system gained popularity outside of the automotive industry. Applications include document management, general marketing, product tracking, item identification, and time monitoring. According to the completed procedure, there will be a decrease in the burden of the government and traffic police, as well as a decrease in accidents and corruption throughout the nation. According to the rule broken by the car driver and the actual fine decided by the government, this programme will immediately deduct the fine from the owner's bank account. The owner of the vehicle will also receive a message from this application detailing the type of rule they broke, the fine associated with it, a photo taken by their mobile device as proof of the violation, the date and time of the violation, and the precise amount they will be receiving in their bank account as payment for the violation. According to this whole process done, there will be reduction in the workload of traffic police and government and also reduction in accidents and corruption too in country.

Secure Cloud Storage Using Different Algorithms in Cryptography

Abstract: Cloud computing offers online services that allow visible servers, dynamic reminiscence swimming pools, and so for a smooth get right of entry. Because dispensed computing is based on the internet, security concerns include information protection, confidentiality, records protection, encryption, and authentication seem. Cloud records garage protection is a primary difficulty. In this examination, we aimed to explore a variety of information safety strategies. The use of cryptographic algorithms is used to solve information protection and privacy troubles in cloud storage. Inside the proposed Hybrid set of rules system RC4, DES & AES Algorithms have been used to enhance data security and privacy. Proposed hybrid system algorithm shield upload and download of statistics from cloud garage. In this case, mystery keys are required for each encryption to do away with encryption. As a result, several parameters have calculated the usage of taking a look at features that encompass encryption time, memory utilization, privacy length, and output to illustrate the performance of the hybrid machine, facts simulations are to be supplied to JAVA, the use of the Eclipse IDE device. The proposed hybrid machine set of rules is used and tested the usage of various record formats such as textual content and photo data. The proposed set of rules is thought to paintings properly to provide additional information safety. Keywords: Cryptography, cloud computing, encryption/decryption Algorithm

Design and Development of a Mobile Application for Teaching Triple Multiplication to Preschool Children.

Due to the difficult educational condition caused by the COVID-19 pandemic, the design and development of mobile phone apps and technologies for teaching and learning are being more considered. Moreover, technology-based infrastructure for the use of distance education is developing rapidly. In this paper, an Android application called Triangular App (TriApp) was designed for teaching multiplication to preschool children. This application uses the Triple Multiplication method in which three numbers (including two numbers, and the product of multiplication of these two numbers) are considered as a group. By repeating and memorizing this group, if any of the numbers are not displayed, the child can guess the missed number. With TriApp, in addition to multiplication, children can also learn division. There is a test and competition section in which the child receives points by giving the correct answers, which increases the children's desire to learn. To evaluate the effectiveness of the TriApp, two preschool children groups were examined in 10 days. The former learned multiplication using TriApp and the later learned it using the multiplication table (repeated additions). The results showed that during test, 100% of the children that used the TriApp could answer asked multiplications correctly and only 40–70% of the children who were trained through multiplication table could answer asked multiplications correctly. Also, the children were satisfied 94% with the application, whereas, they were satisfied only 60% with the multiplication table. This mobile application was developed using Java programming language, Eclipse IDE, and SQLite database.

Automated evolution of feature logging statement levels using Git histories and degree of interest

Logging -- used for system events and security breaches to describe more informational yet essential aspects of software features -- is pervasive. Given the high transactionality of today's software, logging effectiveness can be reduced by information overload. Log levels help alleviate this problem by correlating a priority to logs that can be later filtered. As software evolves, however, levels of logs documenting surrounding feature implementations may also require modification as features once deemed important may have decreased in urgency and vice-versa. We present an automated approach that assists developers in evolving levels of such (feature) logs. The approach, based on mining Git histories and manipulating a degree of interest (DOI) model, transforms source code to revitalize feature log levels based on the "interestingness" of the surrounding code. Built upon JGit and Mylyn, the approach is implemented as an Eclipse IDE plug-in and evaluated on 18 Java projects with $\sim$3 million lines of code and $\sim$4K log statements. Our tool successfully analyzes 99.22% of logging statements, increases log level distributions by $\sim$20%, and increases the focus of logs in bug fix contexts $\sim$83% of the time. Moreover, pull (patch) requests were integrated into large and popular open-source projects. The results indicate that the approach is promising in assisting developers in evolving feature log levels.

A novel approach for Software Architecture Product Line Engineering

A large software system exists in different forms, as different variants targeting different business needs and users. This kind of systems is provided as a set of “independent” products and not as a “single-whole”. Developers use ad-hoc mechanisms to manage variability. We defend a vision of software development where we consider an SPL architecture starting from which the architecture of each variant can be derived before its implementation. Indeed, each derived variant can have its own life. In this paper, we propose a novel approach for Software Architecture Product Line (SAPL) Engineering. It consists of, i) a generic process for recovering an SAPL model which is a product line of “software architectures” from large-sized variants. ii) a forward-engineering process that uses the recovered SAPL to derive new customized software architecture variants. The approach is firstly experimented on thirteen Eclipse variants to create a new SAPL. Then, an intensive evaluation is conducted using an existing benchmark which is also based on Eclipse IDE. Our results showed that we can accurately reconstruct such an SAPL and derive effectively pertinent variants. Our study provides insights that recovering SAPL and then deriving software architectures offers good documentation to understand the software before changing it.

Annotated functional decomposition

AbstractExperiences gained from the domain‐specific courses showed that students focus mostly on how to implement solutions and less on what must be considered within the solution. In the case of information systems‐related courses, students focus on system development using specific languages and frameworks while often disregard the required logical checks and constraints. This paper introduces annotated functional decomposition (AFD) to assist students in overcoming the challenge of understanding the logic of an information system. AFD leverages methodological concepts from computational thinking and represents a problem decomposition approach that is extended with additional levels of decomposition. These levels of decomposition are orthogonal and implemented with annotations that enrich a decomposition with information regarding control and data flow, as well as reuse and implementation details. AFD could be exercised with a supporting AFD Tool developed as an Eclipse IDE plugin that performs syntax and semantic checks along with the generation of UML sequential diagrams. The AFD Tool and its source code are available free of charge. Quantitative and qualitative evaluations of AFD Tool usage during an information systems course revealed that students who used AFD achieved higher average grades than those who used UML for solving the same problems, and moreover that students perceived AFD as easy to understand and use.

Simulation detection based on single photon quantum key distribution protocol

With the continuous development of quantum communication in recent years, quantum key distribution (QKD) has also become a key technology in the field of quantum communication. Due to the limitation of physical resources and experimental conditions, the effectiveness of different communication protocols cannot be accurately analyzed. To solve this problem, a protocol simulation method is proposed, BB84 and B92 protocols are simulated and analyzed on a classic computer by this method. On the Eclipse IDE Developers platform, the simulation program of the quantum communication protocol was written using Java language, the simulation process under the ideal environment and the eavesdropping environment was analyzed with emphasis. The simulation results show that the key distribution efficiency of BB84 is about 50% without eavesdroppers, which is much higher than 25% of B92, and the key distribution efficiency under eavesdropping conditions is about 25% and 18%. The experimental results are consistent with theoretical expectations, verifying the correctness of the protocol simulation, and also proving the feasibility of using computers for quantum communication protocol simulation.

Computation of testing approach in cloud mobility service

At present, programming item turns into a fundamental segment in running numerous partners' exercises. For example, the enterprises for the most part use cloud administrations to execute their significant business usefulness. Be that as it may, by a couple of info's parameter interfacing, this usefulness can be pended. Such requirement postures testing to cover different highlights of disappointment particularly in guaranteeing cloud application. One path is to devise a technique to cover input parameters' qualities dependent on combinatorial testing approach. This method incorporates every single imaginable blend of test contributions for identifying bugs on the system under test (SUT). The paper clarifies the combinatorial covering exhibits to create generally comprehensive testing by demonstrating highlights of test administrations utilising feature IDE module in eclipse IDE. Along these lines, we fabricate the information area model to speak to inclusion of the current portability administration running on NEMo mobility cloud stage. Utilising this model, covering exhibits is connected to create t-way experiments by utilising IPOg calculation, which is executed in a CiTLab. As an experiment the executives, the JUnit testing structure, uses test stubs to approve the test techniques for produced experiments on the predefined administration (SUT).

MylynSDP \u2014 Process - aware artifact filtering based on interest

A software development process is used by software engineers to guide their activities during all phases of the software product development. When executing a software development process, software engineers may lose time and effort while searching for artifacts or changing contexts. This happens, for example, when they need to search for a specific code file in a list of hundreds of files or when they interrupt an activity to execute another but forget specific details and need to re-execute searches related to the previous activity. This impacts their productivity negatively, because extra time and effort are spent into non-productive work. Therefore, automated assistance is required to mitigate or avoid these issues. The Degree of Interest (DOI) function infers an element’s importance in a context, helping software engineers to handle many artifacts. Mylyn, an Eclipse IDE plugin, uses a DOI function on Java documents to assist programmers when looking for code documents during development. However, Mylyn’s DOI function is limited to the implementation phase of software processes and relies on manual task creation. This paper presents MylynSDP, a software Process-aware extension to Mylyn’s DOI function. MylynSDP’s DOI function infers an artifact’s importance during an activity and filters uninteresting artifacts, reducing the time taken to search items and improving productivity. Mylyn code was augmented, and an evaluation study was performed. Seven subjects executed a software process with many artifacts. Exercise times were recorded for productivity analysis. Subjects answered a Technology Acceptance Model (TAM) questionnaire. New task and artifact creation wizards link tasks and artifacts to specification activities and artifacts, respectively. A new interaction event handles context creation, and the DOI function was extended to other software process phases. Exercise time reduction shows a productivity increase. TAM questionnaire answers show a positive overall willingness to adopt MylynSDP and provide evidence that using a DOI function in different software process phases increases productivity. This work advances the state of the art in software engineering by providing additional methods to support artifact search and discovery, context change management, and artifact relevance mechanisms.

Uma Abordagem para Mitigar Ataque Blackhole em Redes Tolerantes a Atrasos e Desconexões utilizando Múltiplos Caminhos

As redes tolerantes a atrasos e desconexões (Delay Tolerant Networks – DTNs) são redes que não precisam de infraestrutura e utilizam da locomoção dos seus nós para se comunicar. Todavia, ataques do tipo Buracos Negros (do inglês, Blackhole) consistem em uma ameaça para o funcionamento destas redes ao descartar pacotes de usuários legítimos. Na literatura há esforços para solucionar o ataque blackhole em DTNs. No entanto, as formas implementadas consomem uma grande quantidade de recursos da rede, pois funcionam por meio da geração de cópias da mensagem. Neste contexto, este trabalho apresenta uma abordagem de mitigação que se utiliza da Mojette e múltiplos caminhos para fragmentar, enviar e recuperar o dado e sem o alto desgaste da rede. A abordagem foi desenvolvida utilizando a IDE (Integrated Development Environment) Eclipse em conjunto com o simulador de ambiente de rede oportunista The ONE (The Opportunistic Network Environment Simulator). Nesse ambiente foram desenvolvidos dois cenários com diferentes situações, a fim de realizar três simulações, visando a avaliar a abordagem que tem como objetivos transmitir e recuperar o pacote ainda que existam descartes. Os resultados obtidos por meio das simulações revelam que a abordagem apresenta uma taxa de remontagem de no mínimo 75%, ainda que a taxa de pacotes perdidos seja de 62,50%.

SpongeBugs: Automatically generating fix suggestions in response to static code analysis warnings

Static code analysis tools such as FindBugs and SonarQube are widely used on open-source and industrial projects to detect a variety of issues that may negatively affect the quality of software. Despite these tools’ popularity and high level of automation, several empirical studies report that developers normally fix only a small fraction (typically, less than 10% (Marcilio et al., 2019) of the reported issues—so-called “warnings”. If these analysis tools could also automatically provide suggestions on how to fix the issues that trigger some of the warnings, their feedback would become more actionable and more directly useful to developers.In this work, we investigate whether it is feasible to automatically generate fix suggestions for common warnings issued by static code analysis tools, and to what extent developers are willing to accept such suggestions into the codebases they are maintaining. To this end, we implemented SpongeBugs, a Java program transformation technique that fixes 11 distinct rules checked by two well-known static code analysis tools (SonarQube and SpotBugs). Fix suggestions are generated automatically based on templates, which are instantiated in a way that removes the source of the warnings; templates for some rules are even capable of producing multi-line patches. Based on the suggestions provided by SpongeBugs, we submitted 38 pull requests, including 946 fixes generated automatically by our technique for various open-source Java projects, including Eclipse UI – a core component of the Eclipse IDE – and both SonarQube and SpotBugs. Project maintainers accepted 87% of our fix suggestions (97% of them without any modifications). We further evaluated the applicability of our technique on software written by students and on a curated collection of bugs. All results indicate that our approach to generating fix suggestions is feasible, flexible, and can help increase the applicability of static code analysis tools.

Safe automated refactoring for intelligent parallelization of Java 8 streams

• Streaming APIs are pervasive in mainstream Object-Oriented programming languages. • Our automated refactoring approach helps in writing efficient stream code. • The approach is based on a novel data ordering and typestate analysis. • Implemented as a plug-in to Eclipse using WALA and SAFE and evaluated on 18 subjects. • Found that 27.68% streams were refactorable, with an average speedup of 3.49. Streaming APIs are becoming more pervasive in mainstream Object-Oriented programming languages and platforms. For example, the Stream API introduced in Java 8 allows for functional-like, MapReduce-style operations in processing both finite, e.g., collections, and infinite data structures. However, using this API efficiently involves subtle considerations such as determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. In this paper, we present an automated refactoring approach that assists developers in writing efficient stream code in a semantics-preserving fashion. The approach, based on a novel data ordering and typestate analysis, consists of preconditions and transformations for automatically determining when it is safe and possibly advantageous to convert sequential streams to parallel and unorder or de-parallelize already parallel streams. The approach was implemented as a plug-in to the popular Eclipse IDE, uses the WALA and SAFE analysis frameworks, and was evaluated on 18 Java projects consisting of ∼1.65M lines of code. We found that 116 of 419 candidate streams (27.68%) were refactorable, and an average speedup of 3.49 on performance tests was observed. The results indicate that the approach is useful in optimizing stream code to their full potential.

A declarative enhancement of JavaScript programs by leveraging the Java metadata infrastructure

Web browsers have evolved into indispensable software platforms for modern applications. Although JavaScript has become a de-facto lingua franca for developing web applications, the JavaScript software development tools and methodologies lag behind those of languages such as Java and C#. In particular, to enhance JavaScript programs with non-functional concerns (e.g., persistence, security, transactions, and logging) JavaScript developers need to modify the source code by hand. Developers then often end up having to maintain the resulting modified version separately for reasons that include poor design, the complexities of program evolution, and fundamental language limitations. The necessity to modify code by hand could be avoided if JavaScript had metadata facilities for developers to specify non-functional concerns declaratively. To address this problem, we present a novel declarative approach, T ransparent A utomated E nhancement for J ava S cript (TAE-JS) that enhances JavaScript programs with the ability to use declarative metadata. The metadata is expressed by means of Java annotations. We have implemented our technique in an open-source plug-in for the Eclipse IDE. This plug-in allows a developer to enhance the original version of a JavaScript program by specifying concerns declaratively using Java annotations. It then analyzes an original version of the program to automatically generate enhanced program versions by adding the declared concerns. We evaluated TAE-JS with four practical examples that enhance JavaScript programs by declaratively adding concerns such as persistence, security, transactions, and logging. In a user study with ten student developers and three professional engineers at PayPal, the participants viewed favorably TAE-JS's flexible declarative enhancement capabilities, seeing them as a valuable mechanism for implementing non-functional concerns in JavaScript programs. Our evaluation results are promising in demonstrating the potential benefits of our approach to complement existing JavaScript development tools.