Phases Of Software Development Research Articles

A systematic literature review on characteristics of the front-end phase of agile software development projects and their connections to project success

ContextSoftware development of new products and services often involves a front-end phase where user needs are analysed, costs and benefits are estimated, and initial plans are created. GoalThis study aims to learn more about how the introduction of agile software development has affected practices and outcomes related to cost and benefit estimation in this front-end phase and to understand better what would improve this phase. MethodWe identified, reviewed and aggregated the results from 42 relevant research articles by searching literature databases and snowballing relevant articles. ResultsThe front-end phase of agile was found to be, on average, similar and just as comprehensive as that of non-agile software development. This may be unfortunate, given the finding that more successful agile software development is connected with less detail in cost estimation and planning-related activities. A less comprehensive front-end phase may be especially beneficial for low-risk agile software development. ConclusionThe results of this review suggest that agile principles, so far, have had a limited influence on the front-end phase. We recommend more flexibility and context-dependency in how the front-end phase of agile software development is conducted, including less comprehensive estimation and planning activities for low-risk software development contexts.

Expediting the design and development of secure cloud-based mobile apps

The adoption and popularity of mobile devices by end-users is partially driven by the increasing development and availability of mobile applications that can aid solving different problems and provide access to services in a wide range of domains or categories, namely healthcare, education, e-commerce or entertainment. While these applications use and benefit from the combination of a wide panoply of technologies from the Internet of Things, fog and cloud computing, data security and privacy are typically not fully taken into account before the creation of many mobile applications or during the software development phases. This paper presents an in-depth approach to modeling attacks on the specific cloud and mobile ecosystem, given its importance in the process of secure application development. Moreover, aiming at bridging the knowledge gap between developers and security experts, this paper presents an alpha version of the security by design for cloud and mobile ecosystem (secD4CloudMobile) framework. secD4CloudMobile is a set of tools that covers cloud and mobile security requirement elicitation (CMSRE), cloud and mobile security best practices guidelines (CMSBPG), cloud mobile attack modeling elicitation (CMAME), and cloud mobile security test specification and tools (CM2ST). The purpose of the framework is to provide cloud and mobile application developers useful readily applicable information and guidelines, striving to bring security engineering and software engineering closer, in a more accessible and automated manner, aiming at the incorporation of security by construction. Finally, the paper presents some preliminary results and discussion.

Measuring and improving software testability at the design level

ContextThe quality of software systems is significantly influenced by design testability, an aspect often overlooked during the initial phases of software development. The implementation may deviate from its design, resulting in decreased testability at the integration and unit levels. ObjectiveThe objective of this study is to automatically identify low-testable parts in object-orientated design and enhance them by refactoring to design patterns. The impact of various design metrics mainly coupling (e.g., fan-in and fan-out) and inheritance (e.g., depth of inheritance tree and number of subclasses) metrics on design testability is measured to select the most appropriate refactoring candidates. MethodThe methodology involves creating a machine learning model for design testability prediction using a large dataset of Java classes, followed by developing an automated refactoring tool. The design classes are vectorized by ten design metrics and labeled with testability scores calculated from a mathematical model. The model computes testability based on code coverage and test suite size of classes that have already been tested via automatic tools. A voting regressor model is trained to predict the design testability of any class diagram based on these design metrics. The proposed refactoring tool for dependency injection and factory method is applied to various open-source Java projects, and its impact on design testability is assessed. ResultsThe proposed design testability model demonstrates its effectiveness by satisfactorily predicting design testability, as indicated by a mean squared error of 0.04 and an R2 score of 0.53. The automated refactoring tool has been successfully evaluated on six open-source Java projects, revealing an enhancement in design testability by up to 19.11 %. ConclusionThe proposed automated approach offers software developers the means to continuously evaluate and enhance design testability throughout the entire software development life cycle, mitigating the risk of testability issues stemming from design-to-implementation discrepancies.

Blood Link

Abstract: In the face of medical emergencies, the rapid and reliable delivery of blood to hospitals can be a matter of life and death. This project presents "BloodLink," a comprehensive solution that leverages modern web technologies and drone delivery systems to streamline the blood supply chain between blood banks and hospitals. The front-end of the system is developed using HTML, CSS, and JavaScript, ensuring a user-friendly interface for hospital staff to post blood group requirements. The backend is powered by the Flask framework of Python, facilitating robust and scalable server-side operations. Utilizing the Google Maps API, our platform dynamically identifies and displays the nearest blood banks in response to hospital requests, ensuring timely and accurate information. For critical situations requiring immediate action, we have integrated an innovative drone delivery system designed to expedite the transportation of blood to the requesting hospitals. The software development phase of the project is complete, and efforts are currently focused on the practical design and implementation of the drone system. This paper details the architecture, development process, and potential impact of "BloodLink" in revolutionizing emergency blood delivery and hospital blood bank coordination.

Design of Data Monitoring System for Intelligent Production Line System Based on Digital Twin

<p>This article establishes a digital twin model for automated production lines. By establishing a more complete digital model, it achieves comprehensive reproduction of the production line, and then extracts and maps data from various production modules in the digital twin to ensure real-time data. In the software development phase, data analysis is conducted on the production and processing process to achieve prediction and management of production. Finally, based on the established digital twin, online simulation virtual debugging can be achieved when flexibly changing production goals, thereby saving production cycles and costs. Therefore, the digital twin platform established in this article has practical significance.</p> <p> </p>

Genetic Programming in Software Engineering: A Systematic Literature Review

Software Engineering is present in all the Software Development Life Cycle phases, implying a systematic and disciplined development process. Nowadays, there are optimization problems within the phases and activities of Software Engineering, problems in which a Genetic Programming (GP) approach can give better results due to its representation of individuals. This Systematic Literature Review aims to analyze the current state of the application of GP in Software Engineering by collecting the phases and activities of software development where Genetic Programming has been used and summarizing the advantages of using this technique. The results show that GP has been used in every phase of software development, although mainly in the construction phase. In addition, a promising area is Program Synthesis, which substantially impacts new fields such as Genetic Improvement.

Requirement elicitation model for global software development vendors

AbstractRequirements elicitation is critical phase of software development that involve direct communication of clients and vendors. In global software development (GSD), importance of requirements elicitation becomes more critical due to geographical distance. The aim of this research is to design and implement requirement elicitation model (REM) where GSD organization can evaluate their organizations and can classify their level of implementation where organization lies. Systematic literature review (SLR) was conducted for the identification of critical success factors (CSFs) from a sample of 70 research articles. Furthermore, the identified CSFs are organized into five levels based on Capability Maturity Model Integration (CMMI) and Software Outsourcing Vendors' Readiness Model (SOVRM). A second SLR was conducted for identifying all possible practices for the implementation of CSFs. To validate the findings of SLR and to find more practices in the view of industrial practitioners for the identified CSFs, a questionnaire survey was conducted in the GSD organizations in which 35 experts, from different countries, contributed. For REM evaluation, we have conducted two case studies. The outcome analysis after conducting case studies confirms that our assessment framework is easier to understand, apply, and successfully critic the strengths and weaknesses of organizations during requirements elicitation in GSD.

Implementasi Blueprint Sistem Informasi Monitoring Pelanggaran Siswa di MAN 1 Padangsidimpuan dalam Bentuk Aplikasi Website

This research intends to incorporate a blueprint design for a student violation information system that was developed in earlier research into a website-based application to record student violations at MAN 1 Padangsidimpuan. The needs analysis, system design, software development, testing, and implementation phases of the website application development process are all included in this study's waterfall methodology. The PHP programming language, the CodeIgniter framework, and the MySQL database were used to create this application. This study produced a web tool that MAN 1 Padangsidimpuan can utilize to more efficiently monitor student infractions. This program is anticipated to assist schools in managing student infractions, delivering transparency to both students and parents, and enhancing student conduct in the classroom.

An Innovative Mobile Application for Wellness Tourism Destination Competitiveness Assessment: The Research and Development Approach

Objectives: This research developed and evaluated the effectiveness of an innovative mobile application for wellness tourism destination competitiveness and also studied the adoption effectiveness of this application. Methods/Analysis: A mixed-methods research and development approach was applied to construct a wellness tourism destination competitiveness evaluation model for qualitative research using in-depth interviews, followed by quantitative research using a questionnaire. Weighted scores of criteria and indicators for wellness tourism destination competitiveness were evaluated by the DEMATEL method. The cut-off points for classifying the competitiveness level were set by K-means cluster analysis, while the internal and external accuracy of the model were validated by the confusion matrix technique and the Kruskal-Wallis test. The innovative mobile application was developed using a linear waterfall conceptual design consisting of five software development phases: requirement, design, implementation, verification, and maintenance. A questionnaire was also used to assess the adoption and commercialization of the innovative mobile application. Findings: Results showed that 1) the model gave high accuracy with the confusion matrix technique at 85.42% and the Kruskal-Wallis test classified destination competitiveness at a significance level of 0.0001; and 2) the level of adoption of the innovative mobile application was high. Target users were interested in purchasing a license as the commercial mode of the program. Novelty/Improvement:This research provides a tool to assess the overall competitiveness of wellness tourism destinations. Results can be used to support decision-making and provide practical suggestions for wellness tourism cluster users to adapt when conducting their own competitiveness assessment. The competitiveness assessment results were accurate and in line with the research objectives. Doi: 10.28991/HIJ-2023-04-03-010 Full Text: PDF

An Exploratory Study Gathering Security Requirements for the Software Development Process

Software development stands out as one of the most rapidly expanding markets due to its pivotal role in crafting applications across diverse sectors like healthcare, transportation, and finance. Nevertheless, the sphere of cybersecurity has also undergone substantial growth, underscoring the escalating significance of software security. Despite the existence of different secure development frameworks, the persistence of vulnerabilities or software errors remains, providing potential exploitation opportunities for malicious actors. One pivotal contributor to subpar security quality within software lies in the neglect of cybersecurity requirements during the initial phases of software development. In this context, the focal aim of this study is to analyze the importance of integrating security modeling by software developers into the elicitation processes facilitated through the utilization of abuse stories. To this end, the study endeavors to introduce a comprehensive and generic model for a secure software development process. This model inherently encompasses critical elements such as new technologies, human factors, and the management of security for the formulation of abuse stories and their integration within Agile methodological processes.

A methodological approach for mobile applications development: MethApp4Mob

The mobile platform has challenged the software development industry, with the consequent need to adapt commonly used methodologies. The study presents MethApp4Mob: a methodological approach based on the traditional technical phases of software development and industrial project experience. It consists of 8 phases: negotiation, planning, design, implementation and alpha testing, beta testing and feedback analysis, promotion, application publication, release and maintenance, and mechanisms to optimize the software development process. MethApp4Mobile tries to keep the balance between a simple and practical approach without losing formality. Validations results encourage its possible application to offer a robust and easy-to-apply methodology in an industrial context.

WATERFALL-ADDIE MODEL: AN INTEGRATION OF SOFTWARE DEVELOPMENT MODEL AND INSTRUCTIONAL SYSTEMS DESIGN IN DEVELOPING A DIGITAL VIDEO LEARNING APPLICATION

To date, researchers are still debating the poor instructional design for certain developed learning applications or courseware in supporting the flexibility to the student. The instructional design is vital in ensuring that learners obtain effective and meaningful to them for a better subject understanding. However, ignoring the functionality and bugs issues in a courseware development should not be practiced. The courseware developer should acknowledge the importance of instructional design and system development model to meet the user and system requirements. Given the resemblances between software development and instructional design phases, researchers could integrate certain importance techniques in software development model’s phases into the ISD model to enhance the design solutions as well as the application technical parts. This paper highlights the integration of Waterfall model as the software development model with ADDIE model which is one of the established ISD models. The integration of Waterfall-ADDIE model was adopted to develop the digital video learning application for university students. A system testing and user acceptance testing were conducted to validate the courseware can meet the system and user requirements. This paper demonstrates all the phases involved in Waterfall model and ADDIE model are interrelated and practically being integrated to propose high-quality development of instructional design learning material.

Just-in-Time Defect Prediction for Self-driving Software via a Deep Learning Model

Edge computing is applied to various applications and is typically applied to autonomous driving software. As the self-driving system becomes complicated and the proportion of software increases, accidents caused by software defects increase. Just-in-time (JIT) defect prediction is a technique that identifies defects during the software development phase, which helps developers prioritize code inspection. Many researchers have proposed various JIT models, but it is difficult to find a case in which JIT defect prediction was performed on edge computing applications. In particular, due to the characteristic of self-driving software, which is frequently updated, there is a high risk of inducing defects into the update process. In this work, we propose a JIT defect prediction model via deep learning for edge computing applications called JIT4EA. Our research goal is to develop an effective model to predict defects in edge computing applications. To do this, we perform defect prediction on self-driving software, a representative edge computing application. We use pre-trained unified cross-modal pre-training for code representation (UniXCoder) to embed commit messages and code changes. We use bidirectional-LSTM(Bi-LSTM) for context and semantic learning. As a result of the experiment, it was confirmed that the proposed JIT4EA performed better than state-of-the-art methods and could reduce the code inspection effort.

A Decade of Intelligent Software Testing Research: A Bibliometric Analysis

It gets harder and harder to guarantee the quality of software systems due to their increasing complexity and fast development. Because it helps spot errors and gaps during the first phases of software development, software testing is one of the most crucial stages of software engineering. Software testing used to be done manually, which is a time-consuming, imprecise procedure that comes with errors and gaps and costs money, time, and effort. Currently, testing professionals routinely automate testing to obtain trustworthy results while saving time, cost, and labor. We’ve also moved the starting point of the software cycle to the developer, and made write tests before even writing code, or what’s known as TDD (Test Driven Development). The use of new artificial intelligence techniques will enable the generation of smart test cases to improve test quality and provide better coverage and accurate results. In this study, we used the Web of Science database to acquire bibliometric data about intelligent software testing papers which were conducted between 2012 and 2022, and we used Biblioshiny from the R bibliomerix package, alongside with VOSViewer in order to analyze the data and extract insights and answer research questions about the authors, articles, journals, organizations, and countries publishing in the field of intelligent software testing. The focus of this study is on scientific progress and collaborative trends in scholarly research, providing a blueprint for showcasing worldwide developments in the realm of intelligent software testing. By gaining a comprehensive understanding of the present state of research on the application of artificial intelligence in software testing, this study can offer valuable insights to software engineers, architects, and researchers in the field.

Lean UX: Applied PSSUQ to Evaluate Less-ON UI/UX Analysis and Design

Indonesian start-up growth continues to show upward growth. Behind the upward movement of change are success statistics that develop contradictions. According to start-up statistics, about 90% of start-ups fail. Up to 75% of unicorn start-ups believe his excellent UI/UX design can boost start-up valuations and additional investment capital. Less-On is a tutoring provider that acts as an intermediary between teachers and students. This research is done by integrating the process of Lean UX methodology into each process present in each phase of software development. The results obtained from this research are final prototypes that have been validated in terms of criticism and suggestions through questionnaires in the form of lessons on start-up branding. The positive user experience and excellent usability will help further the development of the tutor booking application prototype. This plays an important role in the acceptance, satisfaction and efficiency of using this Less-ON application. The user interface has excellent usability for users tested using the PSSUQ, with an overall average score of 2.136, indicating system usability, information quality, interface quality, and overall Satisfaction-based demonstrates that the system is highly acceptable.

VulGraB: Graph‐embedding‐based code vulnerability detection with bi‐directional gated graph neural network

AbstractCode vulnerabilities can have serious consequences such as system attacks and data leakage, making it crucial to perform code vulnerability detection during the software development phase. Deep learning is an emerging approach for vulnerability detection tasks. Existing deep learning‐based code vulnerability detection methods are usually based on word2vec embedding of linear sequences of source code, followed by code vulnerability detection through RNNs network. However, such methods can only capture the superficial structural or syntactic information of the source code text, which is not suitable for modeling the complex control flow and data flow and miss edge information in the graph structure constructed by the source code, with limited effect of neural network model. To solve the above problems, this article proposes a code vulnerability detection method, named VulGraB, which is based on graph embedding and bidirectional gated graph neural networks. VulGraB uses node2vec to convert the program‐dependent graphs into graph embeddings of the code, which contain rich structure information of the source code, improving the ability of features to express nonlinear information to a certain extent. Then the BiGGNN is used for training, and finally the accuracy of the detection results is evaluated using target program. The bi‐directional gated neural network utilizes a bi‐directional recurrent structure, which is beneficial to global information aggregation. The experimental results show that the accuracy of VulGraB is significantly improved over the baseline models on two datasets, with F1 scores of 85.89% and 97.24% being the highest, demonstrating that VulGraB consistently outperforms other effective vulnerability detection models.

Different Machine Learning Algorithms used for Secure Software Advance using Software Repositories

In the present phase of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Cyber Security attacks are significantly growing in today’s modern world of technology and advanced software development. The inclusion of cyber security defense is vital in every phase of software development. Identifying and implementing key relevant cyber security vulnerability controls during the early stages of the software development life cycle, i.e., the requirement phase is especially important. The Common Attack Pattern Enumeration & Classification (CAPEC) is a publicly available software repository from MITRE that currently lists 555 vulnerability attack patterns. As Cyber Security continues to exponentially grow in complexity, the importance of the Machine Learning role to automate the identification of vulnerabilities for various software development is paramount to aid software developers in creating protected software. This paper discusses the conducted survey on different machine learning algorithms used for secure software development using software repositories.

Test case prioritization for changed code using nature inspired optimizer

The software development and maintenance phase succeeded with significant regression testing activity. The software must be re-tested every time it upgrades to preserve its quality. Software testing as a whole is an expensive and tedious task due to resource constraints. Using the prioritization technique implies regression testing to re-test software after it has been modified. In this situation, the prioritization technique can use information acquired about earlier test case executions to generate test case orderings. The approaches for test case prioritization arrange them all in such a sequence that maximizes their efficacy in accomplishing specific goals. This paper presents a hybrid technique for change-testing or regression testing through test case prioritization. The suggested method first generates the test cases, then clustered in untested and unimportant groups using kernel-based fuzzy c-means clustering technique. The appropriate test cases are then considered for prioritization using the grey wolf optimizer. The results compared with the approaches such as ant colony, particle swarm, and genetic algorithm optimization method, and it is observed that the proposed approach efficiency increased by 91% of fault detection rate.

Context-Based Aspect-Oriented Requirement Engineering Model

Mobile applications are context-oriented systems that involve the use of context information while operating. Mobile applications demand tackling context information in the early phase of software engineering. A context-aware system demands a different approach to handling the influence of the context on a system's requirements. Aspect-oriented Requirement Engineering separates concerns throughout requirements, called crosscutting concerns, in the early phase of software development to improve the modularity of complex applications. Capturing requirements embedded within context is a challenging procedure. This study aimed to identify such contextual characteristics of requirements in the early phase of software engineering, using natural language processing techniques, by proposing Context-Based Aspect-Oriented Requirement Engineering (CB-AORE) to visualize the existence of crosscutting concerns. CB-AORE performs context modeling to analyze the context dependency with base requirements and helps the analyst to visualize the correlation of functional and non-functional requirements with context. A case study analyzed the identification of context and its use to identify crosscutting concerns.

Performance assessment and analysis of development and operations based automation tools for source code management

<span lang="EN-US">Development and operations (DevOps), an accretion of automation tools, efficiently reaches the goals of software development, test, release, and delivery in terms of optimization, speed and quality. Diverse set of alternative automation tools exist for different phases of software development, for which DevOps adopts several selection criteria to choose the best tool. This research paper represents the performance evaluation and analysis of automation tools employed in the coding phase of DevOps culture. We have taken most commonly followed source code management tools-BitBucket, GitHub actions, and GitLab into consideration. Current work assesses and analyzes their performance based on DevOps evaluation criteria that too are categorized into different dimensions. For the purpose of performance evaluation, weightage and overall score is assigned to these criteria based on existing renowned literature and industrial case study of TekMentors Pvt Ltd. On the ground of performance outcome, the tool with the highest overall score is realized as the best source code automation tool. This performance analysis or measure will be a great benefit to our young researchers/students to gain an understanding of the modus operandi of DevOps culture, particularly source code automation tools. As a part of future research, other dimensions of selection criteria can also be considered for evaluation purposes.</span>