Phases Of Development Life Cycle Research Articles

A Review of Non-Functional Requirements Analysis Throughout the SDLC

To date, unquestionable efforts have been made, both in academia and industry, to facilitate the development of functional requirements (FRs) throughout the different phases of the software development life cycle (SDLC). Functional requirements are understood to mean the users’ needs pertaining to the services to be rendered by a software system. For example, semi-formal or graphically based approaches such as UML, and mathematically based or formal approaches such as Z and related tools have all been developed with the intention of addressing FRs. In the same vein, most of the proposed software methodologies, for instance, agile software development and model-driven software development, primarily target functional requirements. Considering the importance and even the criticality of non-functional requirements describing the quality of software systems and the constraints upon them, similar progress would be expected for their development. However, it appears that making headway with NFRs has been more challenging due to the complexity of the requirements. In this regard, the main purpose of this work is to unveil (from the academic perspective) the current state of development of NFRs through the review of publications carefully selected from five online databases.

Development of SWAT, a Solar and Wind Powered Agricultural Pest Trap-peller

In the Philippines, pest infestation has been a major challenge for farmers which led to significant losses in crop yields and income due to the lack of access to effective pest management techniques. To address this issue, the researchers developed a pest management device powered by solar and wind energy used to minimize pest infestation in the fields. This research utilized the phases of the System Development Life Cycle in developing the prototype. This system utilized an ultrasonic speaker and ultraviolet light (UV) bug zapper as the main components of the device. The device was able to collect insects and moths in the farmland and was able to decrease the number of insects and moths collected from 3 kg to 0.01 kg in one week. The researchers were also able to prove that the ultrasonic speaker with a frequency of 15 kHz was effective for deterring birds up to 40 meters away from the device. Comparison between the traditional method and the developed prototype showed that the latter requires less amount of work and human intervention thus, eliminating the adverse health effects from continuous exposure to loud sounds and insecticides used in the traditional ways. End-user evaluation results showed excellent functionality, very easy to use, high-performance efficiency, strong security, and very easy to maintain proving the prototype’s effectiveness as an alternative solution to the problem of pest infestation in farmlands.

Innovating in the Detection of Depression in Young Adults: Development of a Mobile Application Based on the Enhanced Cascade Model

Depression is a disorder that usually begins in early adulthood, having a duration according to the World Health Organization (WHO) from a couple of months to a couple of years, however, in approximately 20% of cases there is a chronic evolution that results in a high mortality rate by suicide due to lack of adequate treatment. Therefore, the objective of this project is to facilitate the tools for an adequate care of depression, mainly in young adults through the development of a mobile application that monitors patients. To carry out the project, the enhanced cascade model was implemented, because the feedback between the different phases of the system development life cycle is important to make the appropriate modifications. The application of interviews and the use of brainstorming were the selected way in which all the system requirements were designed and the goals to be met by the system were adequately defined.

Method-level Bug Prediction: Problems and Promises

Fixing software bugs can be colossally expensive, especially if they are discovered in the later phases of the software development life cycle. As such, bug prediction has been a classic problem for the research community. As of now, the Google Scholar site generates ∼113,000 hits if searched with the “bug prediction” phrase. Despite this staggering effort by the research community, bug prediction research is criticized for not being decisively adopted in practice. A significant problem of the existing research is the granularity level (i.e., class/file level) at which bug prediction is historically studied. Practitioners find it difficult and time-consuming to locate bugs at the class/file level granularity. Consequently, method-level bug prediction has become popular in the past decade. We ask, are these method-level bug prediction models ready for industry use? Unfortunately, the answer is no . The reported high accuracies of these models dwindle significantly if we evaluate them in different realistic time-sensitive contexts. It may seem hopeless at first, but, encouragingly, we show that future method-level bug prediction can be improved significantly. In general, we show how to reliably evaluate future method-level bug prediction models and how to improve them by focusing on four different improvement avenues: building noise-free bug data, addressing concept drift, selecting similar training projects, and developing a mixture of models. Our findings are based on three publicly available method-level bug datasets and a newly built bug dataset of 774,051 Java methods originating from 49 open-source software projects.

Generative AI in Responsible Conversational Agent Integration: Guidelines for Service Managers

Responsible integration of conversational agents (CAs) like chatbots is crucial for service firms to mitigate risks and foster positive outcomes. This article provides managerial guidelines through a Corporate Digital Responsibility (CDR) lens, focusing on CDR Culture, Management Structure, and Digital Governance across the service firm, software provider, and customers/society. It examines how organizational sensemaking processes of creation, interpretation, and enactment are triggered by CA-related issues and events. The research highlights the role of generative AI (GenAI) in implementing CDR factors and responsible CA software development lifecycle phases during development and integration. Guidelines are provided for leveraging GenAI to enhance CDR Culture, incorporate ethical considerations into CDR Management Structure, and enable robust Digital Governance mechanisms to prioritize customer/societal well-being. A multilevel framework illustrates reinforcing the guidelines through organizational sensemaking processes, and fostering responsible CA integration aligned with ethical principles and societal values.

Traffic Light Control Systems: Requirements Engineering

Requirements Engineering (RE) is the most important activity and pivot phase in the software development life cycle. It consists mainly of requirements analysis, design, and specification. The application of RE in a business domain aims to generalize its different specific approaches by meta-models under which each specific approach is an instance or specialization. RE is being applied more and more in different business domains, and its application benefits are found to be valuable. Despite these active RE applications and their added values in different domains, the Traffic Light Control (TLC) domain has not yet been approached, regardless of its everyday interest. The work presented in this paper is part of a TLC enhancement project that explores RE's potential contributions to leveraging TLC quality. Thus, in this work, different recent specific approaches to TLC are analyzed, and a large part of this business domain's functional/ non-functional requirements are elicited and specified. Moreover, the traffic context-aware traffic light control systems (CTLC) are also considered. They are deeply analyzed and compared to the typical TLC systems. As a primary result, the tangling of different concerns was stated, and a separate concerns paradigm was applied. This leads to requirements specification through separated agents. This should lead to TLC and CTLC systems development and maintenance cost reduction. An important agent dealing with security aspects and their evolving technologies is introduced. The impact evaluations of the RE on some TLC and CTLC current approaches are also presented.

SOFTWARE TESTING AND ITS ASPECTS

The software testing topic is becoming more and more popular. This article discusses the software testing concept, software testing types, how it works and where it is applied. Software testing is the most important phase of the software development life cycle, so this article is about ensuring the quality of all software applications types by executing certain types of testing methods and streamlined software testing processes. The object of the work is the software testing process. The subject of the research is software testing and its life cycle. The purpose of the work is to review the software testing con-cept, testing types, software testing capabilities, software testing stages, as well as current state presentation of issue. The theoretical method was chosen as the research method. In this paper, an optimized testing process is considered, which considers all testing life cycle stages and also considers the testing types. The article describes the current state of issue. The work objectives have been achieved. The assigned tasks have been completed.

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.

Nurses' Roles in mHealth App Development: Scoping Review.

Although mobile health (mHealth) apps for both health consumers and health care providers are increasingly common, their implementation is frequently unsuccessful when there is a misalignment between the needs of the user and the app's functionality. Nurses are well positioned to help address this challenge. However, nurses' engagement in mHealth app development remains unclear. This scoping review aims to determine the extent of the evidence of the role of nurses in app development, delineate developmental phases in which nurses are involved, and to characterize the type of mHealth apps nurses are involved in developing. We conducted a scoping review following the 6-stage methodology. We searched 14 databases to identify publications on the role of nurses in mHealth app development and hand searched the reference lists of relevant publications. Two independent researchers performed all screening and data extraction, and a third reviewer resolved any discrepancies. Data were synthesized and grouped by the Software Development Life Cycle phase, and the app functionality was described using the IMS Institute for Healthcare Informatics functionality scoring system. The screening process resulted in 157 publications being included in our analysis. Nurses were involved in mHealth app development across all stages of the Software Development Life Cycle but most frequently participated in design and prototyping, requirements gathering, and testing. Nurses most often played the role of evaluators, followed by subject matter experts. Nurses infrequently participated in software development or planning, and participation as patient advocates, research experts, or nurse informaticists was rare. Although nurses were represented throughout the preimplementation development process, nurses' involvement was concentrated in specific phases and roles.

Empirical exploration of critical challenges of requirements implementation in global software development

AbstractRequirements collection is difficult and a critical phase of software development life cycle, in particular in a global software development (GSD) environment. In GSD, clients and vendors are physically separated such that there exist challenges such as lack of face‐to‐face communication, language differences, culture variation, and time zone differences. The objective of current research is to identify critical challenges of requirement engineering in GSD. There is no work done yet to empirically analyze all possible challenges via questionnaire survey from software industries. This research paper empirically investigates and analyze the identified challenges from systematic literature review (SLR) based on a questionnaire survey. For this purpose, 50 respondents from different countries are organized. From this research, 13 challenges during requirements implementation in the context of GSD have been identified previously via SLR. These challenges were then evaluated using an empirical approach of questionnaire survey. The results from respondents were analyzed based on type of respondents, level of experience of respondents, and from client–vendor perspective. Finally, the challenges were prioritized based on its frequency of occurrences from the SLR and the questionnaire survey. The relationships between the challenges and the survey results were evaluated using the Spearman's correlation coefficient. The results produced a 0.835 Spearman's correlation coefficient at significance level ρ = 0.000, which showed a strong positive correlation between the outcome of SLR and survey with no significant difference. The implication of this research work lies in both fundamental and practical perspective. The prioritized set of challenges was provided based on SLR, and questionnaire survey acts as a knowledge base for both researchers and industrial practitioners. This work will help researchers to identify challenges in GSD projects or other software engineering areas.

Evaluation of requirement engineering best practices for secure software development in GSD: An ISM analysis

AbstractTechnological advancement makes the world a global village. Security is an evergreen and everlasting area, because of the continuous threat from Hackers and Crackers. The immense use of software systems has modernized human society in every aspect. Thus, it is crucial to devise new processes, techniques, and tools to support teams in the development of secure code from the early stages of the software development process, while potentially reducing the costs and shortening the time to market. Considering the significance of software security, it is important to consider the security practices from the early phase of the software development life cycle (SDLC), that is, requirements engineering (RE). Hence, this study aims to identify and categorize RE practices important to apply for secure software development (SSD) in a geographically distributed development environment. To study the RE practices concerning SSD, we conducted a questionnaire survey with industrial experts in the global software development (GSD) context.Furthermore, the interpretive structure modeling (ISM) approach was applied to evaluate the relationship between the RE security practice core categories. This paper identifies 70 practices and classifies them into 11 fundamental dimensions (categories) to assist GSD organizations in specifying the requirements for SSD. The ISM results show that the “Awareness of Secure Requirement Engineering (SRE)” category has the most decisive influence on the other 10 core categories of the identified RE security practices. With the help of empirical evidence and the ISM approach, this work attempts to identify potential security practices and to give a set of secure RE practices that can be used to improve the security of the software development process.

Maturity model for secure software testing

AbstractSecurity is an essential attribute of high‐quality software. However, effectively incorporating security practices into different phases of the software development life cycle (SDLC) remains challenging. Owing to less mature secure testing processes, organizations are prone to ineffective testing practices for defect detection, including severe security‐related failures. Thus, in this study, we present a maturity model for secure software testing (MMSST) to assist software development organizations in improving the secure testing of software applications. We conducted a multivocal literature review and identified 68 primary studies from the formal and gray literature. Then, based on the available evidence, 27 process areas were identified to develop the proposed MMSST. The MMSST includes five main categories: governance, contrive and design, execution, deployment and configuration, and mature. The MMSST was subsequently evaluated using case studies related to practical environments. Results demonstrate that the proposed MMSST is useful for estimating the maturity level of an organization with respect to the secure testing phase of the SDLC. The participants of the case studies also agreed that the proposed MMSST is useful in terms of structure, user satisfaction, and ease of use. We believe that the proposed MMSST can help organizations evaluate and improve software security testing practices. In addition, the proposed MMSST is expected to provide researchers and industry practitioners with an effective foundation for developing new secure testing approaches and tools.

Towards the Integration of Security Practices in Agile Software Development: A Systematic Mapping Review

Software development must be based on more than just the experience and capabilities of your programmers and your team. The importance of obtaining a quality product lies in the risks that can be exploited by software vulnerabilities, which can jeopardize organizational assets, consumer confidence, operations, and a broad spectrum of applications. Several methods, techniques, and models have been suggested and developed to address software security. However, only a few have solid evidence for creating secure software applications. The main objective of this paper is to survey the literature for methods or models suitable for considering the integration of security in all or some of the phases of the software development life cycle and which ones are most considered or neglected. This study represents the beginning of research to generate a methodology that integrates security practices in agile software development, allowing inexperienced developers to create more secure applications.

Corruption Propensity and Mitigation at Different Phases of Infrastructure Development

Corruption is a social evil that resonates far and deep in societies, eroding trust in government, weakening the rule of law, impairing economic development, and exacerbating poverty, social tension, and inequality. Each phase of infrastructure development life cycle has peculiar vulnerabilities to corrupt practices. Pre-emptive identification of avenues for corrupt actions at each phase can serve as a starting point for new initiatives or for renewed reinforcement of existing corruption mitigation programs. The existing literature on corruption does not address the issue from an infrastructure life cycle perspective. The objective of this paper is to identify opportunities at each phase of the infrastructure life cycle where stakeholders and actors can help undertake specific actions to mitigate corruption. The paper’s methodology characterizes corruption and mitigation measures in a framework based on the phases of infrastructure development life cycle and then connects these measures to the relevant actors and stakeholders.

Security Best Practices: A Critical Analysis Using IoT as a Case Study

Academic research has highlighted the failure of many Internet of Things (IoT) product manufacturers to follow accepted practices, while IoT security best practices have recently attracted considerable attention worldwide from industry and governments. Given current examples of security advice, confusion is evident from guidelines that conflate desired outcomes with security practices to achieve those outcomes. We explore a surprising lack of clarity, and void in the literature, on what (generically) best practice means, independent of identifying specific individual practices or highlighting failure to follow best practices. We consider categories of security advice, and analyze how they apply over the lifecycle of IoT devices. For concreteness in discussion, we use iterative inductive coding to code and systematically analyze a set of 1,013 IoT security best practices, recommendations, and guidelines collated from industrial, government, and academic sources. Among our findings, of all analyzed items, 68% fail to meet our definition of an (actionable) practice, and 73% of all actionable advice relates to the software development lifecycle phase, highlighting the critical position of manufacturers and developers. We hope that our work provides a basis for the community to better understand best practices, identify and reach consensus on specific practices, and find ways to motivate relevant stakeholders to follow them.

Secure Global Software Development: A Practitioners’ Perspective

Global software development (GSD) is rapidly becoming standard practice in the software industry due to its many potential benefits. However, one of the biggest challenges in GSD projects is to explicitly include security in the different phases of the global software development life cycle (GSDLC). To make GSD projects secure and successful, it is necessary to identify secure software development (SSD) practices vital to GSD project success. This article aims to identify SSD practices critical for GSD projects. To do this, we selected 36 security practices vital to the security of non-GSD projects from existing scientific and grey literature on software security. From the identified security practices, we shortlisted the security practices which are critical for GSD projects based on practitioners’ opinions using an online survey. Fifty-four GSD practitioners participated in this survey. Participants who evaluated these practices were asked to score each SSD practice on a four-point scale to indicate its relevance to GSD projects. The results obtained from the survey uncovered critical SSD practices that are primarily applicable to GSD projects. Our findings reveal variations of opinion among GSD practitioners with varying experience and company size regarding the importance of selected security practices for GSD. According to study findings, 16/36 practices are critical for GSD projects. These identified security practices belong to various phases of GSDLC.

A systematic mapping study of software performance research

SummaryContext: Software performance is crucial for ensuring the quality of software products. As one of the non‐functional requirements, the few efforts devoted to software performance have often been neglected until a later phase in the software development life cycle (SDLC). The lack of clarity of what software performance research literature is available prevents researchers from understanding what software performance research fields are available. It also creates difficulty for practitioners to adopt state‐of‐the‐art software performance techniques. Software performance research is not as organized as other established research topics such as software testing. Thus, it is essential to conduct a systematic mapping study as a first step to provide an overview of the latest research literature available in software performance. Objective: The objective of this systematic mapping study is to survey and map software performance research literature into suitable categories and to synthesize the literature data for future access and reference. Method: This systematic mapping study conducts a manual examination by querying research literature in noble journals and proceedings in software engineering in the past decade. We examine each paper manually and identify primary studies for further analysis and synthesis according to the pre‐defined inclusion criteria. Lastly, we map the primary studies based on their corresponding classification category. Results: This systematic mapping study provides a state‐of‐the‐art literature mapping in software performance research. We have carefully examined 222 primary studies out of 2000+ research literature. We have identified six software performance research categories and 15 subcategories. We generate the primary study mapping and report five research findings. Conclusions: Unlike established research fields, it is unclear what types of software performance research categories are available to the community. This work takes the systematic mapping study approach to survey and map the latest software performance research literature. The study results provide an overview of the paper distribution and a reference for researchers to navigate research literature on software performance.

Review of ways to apply machine learning methods in software engineering

This article reviews the integration of machine learning (ML) techniques into Software Engineering (SE) across various phases of the software development life cycle (SDLC). The purpose is to investigate the applications of ML in SE, analyze its methodologies, present findings, and draw conclusions regarding its impact. The study categorized ML applications in SE and assessed the performance of various ML algorithms. Authors identified ML applications in SDLC phases, including requirements analysis, design, implementation, testing, and maintenance. ML algorithms, such as supervised and unsupervised learning, are employed for tasks like software requirement identification, design pattern recognition, code generation, and automated testing. In summary, we find that ML-based techniques are experiencing a substantial surge in adoption within the field of software engineering. Nevertheless, it is evident that substantial endeavors are needed to establish thorough comparisons and synergies among these approaches, perform meaningful evaluations grounded in detailed real-world implementations that are applicable to industrial software development. Therefore, our key takeaway is the necessity for a shift in focus towards reproducible research, prioritizing this over isolated novel concepts. Failure to do so may result in the limited practical implementation of these promising applications.

Про деякі аспекти інженерії програмних систем з елементами штучного інтелекту

AI-based software systems are rapidly spreading in various business areas. In this context, the unavoidable convergence of the Software Engineering and Artificial Intelligence and Machine Learning (AI/ML) disciplines is considered an obvious and one of the following significant challenges within the engineering process. The life cycle, models, and technologies of AI/ML elements are pretty specific, and this should be considered in software engineering to ensure their performance and compliance with business needs. AI/ML applications have some distinct characteristics compared to traditional software applications. Thus, several challenges and risk factors regarding AI/ML applications appear to software developers. To study the common challenges in AI/ML application development, we used two different perspectives: software engineering and machine learning. AI/ML applications, like other software systems, need a well-defined software engineering process for their development and maintenance. We discussed challenges and recommendations for different phases of the software development life cycle for ML applications, particularly requirement engineering, design, implementation, integration, testing, and deployment. AI/ML application development has specific aspects to consider as a software development project. We discussed the characteristics and recommendations concerning problem formulation, data acquisition, preprocessing, feature extraction, model building, evaluation, model integration and deployment, model management, and ethics in AI/ML development. In the work, there were formulated recommendations for each analyzed challenge that should be useful for software developers. The next stage of this research is the compilation of detailed systematic guidelines for the software development process for AI/ML systems.

Security risks of global software development life cycle: Industry practitioner's perspective

AbstractSoftware security has become increasingly important because the malicious attack and other hacker risks of a computer system have grown popularity in the last few years. As a result, several researchers have examined security solutions as early as the requirement engineering phase. With the growth of the software business and the internet, there is a need to understand the security risks against each phase of the software development life cycle (SDLC). This study aims to empirically investigate and prioritize the risks that could negatively impact the software security aspects of SDLC in the context of global software development (GSD). To achieve the study objectives, we conducted an industrial empirical study to determine the impact of software security threats against each phase of SDLC. Furthermore, the fuzzy analytical hierarchy process (FAHP) was used to prioritize the list of software security risks against the SDLC. The results and analysis of this study provide a ranked‐based decision‐making framework, which assists the practitioners in considering the most critical security risks on priority. The results show “improper plan for secure requirement identification, inception, authentication, authorization, and privacy,” “lack of threat models updating,” “lack of output validation,” “lack of certification in the final release and archive,” and “spoofing” as the top‐ranked security risks of SDLC in GSD. In addition, the application of FAHP is novel in this domain as it is helpful to address multicriteria decision‐making problems.