Software Development Life Cycle Research Articles

Algorithm 1039: Automatic Generators for a Family of Matrix Multiplication Routines with Apache TVM

We explore the utilization of the Apache TVM open source framework to automatically generate a family of algorithms that follow the approach taken by popular linear algebra libraries, such as GotoBLAS2, BLIS, and OpenBLAS, to obtain high-performance blocked formulations of the general matrix multiplication ( gemm ). In addition, we fully automatize the generation process by also leveraging the Apache TVM framework to derive a complete variety of the processor-specific micro-kernels for gemm . This is in contrast with the convention in high-performance libraries, which hand-encode a single micro-kernel per architecture using Assembly code. In global, the combination of our TVM-generated blocked algorithms and micro-kernels for gemm (1) improves portability, maintainability, and, globally, streamlines the software life cycle; (2) provides high flexibility to easily tailor and optimize the solution to different data types, processor architectures, and matrix operand shapes, yielding performance on a par (or even superior for specific matrix shapes) with that of hand-tuned libraries; and (3) features a small memory footprint.

Implementation of a reinforcement learning system with deep q network algorithm in the amc dash mark i game

Reinforcement learning is a branch of artificial intelligence that trains algorithms using a trial-and-error system. Reinforcement learning interacts with its environment and observes the consequences of its actions in response to rewards or punishments received. Reinforcement Learning uses information from every interaction with its environment to update its knowledge. The problem identified from this research is the lack of consistency, which is not always the same for Non-Player Characters (Agents) in the process of exploring an environment (Game environment). This research uses the Software Development Life Cycle (SDLC) Waterfall model method to train Non Player Characters (Agents) in the Amc Dash Mark I Game which uses the Deep Q Network (DQN) algorithm in several stages. Training results show improvements in model performance over time. The average duration of the episode and average reward episode showed an increase of 7.75 to 24.7, while the exploration rate decreased to 0.05. This indicates that the model has experienced learning and is improving to achieve better rewards by performing fewer actions. The lower loss also shows that the model has succeeded in reducing prediction errors and improving prediction capabilities.

UNLEASHING THE POWER OF DEVOPS IN SOFTWARE DEVELOPMENT

In the dynamic landscape of modern software development, the adoption of DevOps practices has emerged as a transformative force, revolutionizing the way organizations conceive, build, deploy, and maintain software applications. This abstract provides an overview of the key concepts, benefits, and challenges associated with unleashing the power of DevOps in software development. DevOps, an amalgamation of "Development" and "Operations," is a cultural and collaborative approach that aims to bridge the gap between software development and IT operations. The fundamental philosophy of DevOps is centred on fostering seamless communication, collaboration, and integration among cross-functional teams, thereby accelerating the software development lifecycle. This abstract explores the core principles that underpin DevOps, emphasizing the importance of automation, continuous integration, continuous delivery, and continuous monitoring in achieving operational excellence. The iterative nature of DevOps enables organizations to respond swiftly to changing requirements, reduce time-to-market, and enhance the overall quality of software products. Furthermore, the abstract delves into the transformative impact of DevOps on organizational culture, emphasizing the shift from traditional siloed structures to a more holistic, collaborative environment. Case studies and industry examples will be presented to illustrate successful implementations of DevOps practices, showcasing measurable improvements in productivity, efficiency, and customer satisfaction. Despite the numerous advantages, adopting DevOps is not without its challenges. The abstract will address common hurdles faced by organizations, including cultural resistance, toolchain integration complexities, and the need for skilled personnel. Strategies to overcome these challenges will be discussed, empowering organizations to navigate the DevOps journey effectively.

Multi-type requirements traceability prediction by code data augmentation and fine-tuning MS-CodeBERT

Requirement traceability is a crucial quality factor that highly impacts the software evolution process and maintenance costs. Automated traceability links recovery techniques are required for a reliable and low-cost software development life cycle. Pre-trained language models have shown promising results on many natural language tasks. However, using such pre-trained models for requirement traceability needs large and quality traceability datasets and accurate fine-tuning mechanisms. This paper proposes code augmentation and fine-tuning techniques to prepare the MS-CodeBERT pre-trained language model for various types of requirements traceability prediction including documentation-to-method, issue-to-commit, and issue-to-method links. Three program transformation operations, namely, Rename Variable, Swap Operands, and Swap Statements are designed to generate new quality samples increasing the sample diversity of the traceability datasets. A 2-stage and 3-stage fine-tuning mechanism is proposed to fine-tune the language model for the three types of requirement traceability prediction on provided datasets. Experiments on 14 Java projects demonstrate a 6.2% to 8.5% improvement in the precision, 2.5% to 5.2% improvement in the recall, and 3.8% to 7.3% improvement in the F1 score of the traceability prediction models compared to the best results from the state-of-the-art methods.

Aplikasi e-Arsip Berbasis Android untuk Guru dan Karyawan di Sekolah Menengah Kejuruan

Current technological developments have experienced very rapid developments. Its use is not only to help work in an agency or company but can also create accuracy, speed, and completeness. This also impacts an agency such as SMK Negeri 1 Purwokerto, which wants to utilize technology to manage archival documents in electronic form or electronic archives (e-Archives). E-Archives are a system for collecting and storing information in the form of electronic documents to make them easy to view, manage, find, and reuse. The need for an electronic archive management system is because the archive management process at the agency still uses manual methods by storing archival documents in physical form in a storage area. This method causes several problems with archival documents, such as loss, damage, and difficulty searching for documents. This problem requires a solution to overcome it, namely by building an Android-based application or system as an application for recording and managing archival documents. The development method used is the Software Development Life Cycle (SDLC) method with the waterfall model. The type of research carried out is research and development of the Research and Development method. The final result of this research is to produce a product in the form of an Android-based application for managing archival documents. This research was successfully carried out by referring to functionality testing, which was carried out using the Black Box Testing method.

AN EMPIRICAL STUDY TO IDENTIFY AND WEIGH THE RISK ATTRIBUTES THROUGH THE APPLICATION OF FMEA IN THE SOFTWARE INDUSTRY WITH FOCUS ON MSME AND SME IT FIRMS IN BENGALURU REGION

Uncertain events can occur anytime during the lifecycle of software. An organization's readiness to deal with these events determines its ability to mitigate the potential losses. Risk management is an essential component of the lifecycle process and is given due diligence in a typical multi-tiered environment of the technological giants. On the contrary, small IT firms, especially the ones falling under the belt of SME and MSME sectors, often follow a reactive approach to risk management. Consequently, the probable losses incurred can be huge and sometimes even challenge the firm's existence in the market. Considering the above issue, we have put forward this paper after performing an empirical study targeting only the Bengaluru region's MSME/SME IT firms. The study was primarily conducted with two objectives (1) to identify the risk attributes and (2) to determine the effectiveness of FMEA techniques and weigh these attributes through the FMEA process. 43 failure modes were identified, which were ranked by a panel of 12 QA experts based on their past project experiences. The top ten failure modes identified by the process will be considered for future analysis and for determining the mitigation steps as a part of the risk management process, particularly for start-ups.

Advancing green computing: Practices, strategies, and impact in modern software development for environmental sustainability

Advancing Green Computing: Practices, Strategies, and Impact in Modern Software Development for Environmental Sustainability explores the evolving landscape of green computing within the realm of software development, emphasizing the imperative for environmentally sustainable practices. In response to escalating environmental concerns, the computing industry is undergoing a paradigm shift towards reducing its carbon footprint and mitigating ecological impacts. This shift is particularly crucial in software development, given its pervasive influence on technological ecosystems. The review delves into the multifaceted dimensions of green computing, elucidating various practices and strategies that are instrumental in fostering environmental sustainability. From optimizing code efficiency to embracing energy-efficient computing architectures, the review underscores the diverse approaches available to software developers in minimizing resource consumption and carbon emissions. Furthermore, it examines the broader ramifications of these practices, emphasizing their potential to reshape the software industry's ecological footprint and contribute to global efforts for environmental conservation. Moreover, the review highlights the symbiotic relationship between green computing and modern software development methodologies. It elucidates how principles such as agile development and DevOps can be synergistically integrated with green computing practices to enhance sustainability throughout the software development lifecycle. By adopting an interdisciplinary approach that integrates environmental considerations into software design, development, and deployment processes, organizations can catalyze transformative changes towards a greener computing ecosystem. The review also investigates the tangible impact of green computing practices on environmental sustainability metrics. Through case studies and empirical analyses, it showcases the efficacy of various strategies in reducing energy consumption, carbon emissions, and electronic waste generation. Additionally, it discusses the economic and societal benefits accrued from adopting environmentally sustainable practices, ranging from cost savings to enhanced corporate social responsibility. Advancing Green Computing: Practices, Strategies, and Impact in Modern Software Development for Environmental Sustainability provides a comprehensive overview of the evolving landscape of green computing within the context of software development. It elucidates the myriad opportunities and challenges associated with fostering environmental sustainability in the computing industry and underscores the transformative potential of integrating green computing principles into modern software development practices.

Tools, techniques, and trends in sustainable software engineering: A critical review of current practices and future directions

The quest for sustainability has extended its reach into the realm of software engineering, prompting an exploration of tools, techniques, and emerging trends to mitigate the environmental impact of software development and operation. This review provides a critical review of current practices and future directions in sustainable software engineering. In recent years, the software industry has recognized the need to address the environmental footprint of software systems, considering factors such as energy consumption, resource utilization, and carbon emissions. Consequently, a plethora of tools and techniques have emerged to support sustainable software development processes. These range from energy-efficient programming languages and frameworks to eco-friendly software architectures and design patterns. Moreover, methodologies such as Green Software Engineering (GSE) and Sustainable Software Development (SSD) have gained traction, emphasizing the integration of sustainability considerations throughout the software development lifecycle. By adopting practices like green requirements engineering, energy-aware testing, and eco-design principles, organizations can optimize their software systems for reduced environmental impact without compromising functionality or performance. Furthermore, trends in sustainable software engineering extend beyond traditional development practices. The rise of cloud computing, edge computing, and Internet of Things (IoT) technologies presents both challenges and opportunities for sustainability. Techniques such as serverless computing and containerization offer potential benefits in terms of resource efficiency and scalability, while also introducing new considerations regarding data center energy consumption and electronic waste management. Looking ahead, the future of sustainable software engineering is marked by innovation and collaboration. Emerging technologies such as artificial intelligence (AI) and blockchain hold promise for optimizing resource allocation, enhancing energy efficiency, and fostering transparency in sustainability efforts. Additionally, interdisciplinary collaboration between software engineers, environmental scientists, and policymakers will be essential in shaping a more sustainable digital ecosystem. The journey towards sustainable software engineering involves a multifaceted approach encompassing tools, techniques, and ongoing adaptation to evolving trends. By critically evaluating current practices and embracing future directions, the software industry can contribute to a more environmentally responsible and resilient future.

Recommendation of Machine Learning Techniques for Software Effort Estimation using Multi-Criteria Decision Making

 For the development of the software industry, Software Effort Estimation (SEE) is one of the essential tasks. Project managers can overcome budget and time overrun issues by accurately estimating a software project's development effort in the software life cycle. In prior studies, a variety of machine learning methods for SEE modeling were applied. The outcomes for various performance or accuracy measures are inconclusive. Therefore, a mechanism for assessing machine learning approaches for SEE modeling in the context of several contradictory accuracy measures is desperately needed. This study addresses selecting the most appropriate machine learning technique for SEE modeling as a Multi-Criteria Decision Making (MCDM) problem. The machine learning techniques are selected through a novel approach based on MCDM. In the proposed approach, three MCDM methods- Weighted Aggregated Sum Product Assessment (WASPAS), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) were applied to determine the ranking of machine learning techniques on SEE performance based on multiple conflicting accuracy measures. For validating the proposed method, an experimental study was conducted over three SEE datasets using ten machine-learning techniques and six performance measures. Based on MCDM rankings, Random Forest, Support Vector Regression, and Kstar are recommended as the most appropriate machine learning techniques for SEE modeling. The results show how effectively the suggested MCDM-based approach can be used to recommend the appropriate machine learning technique for SEE modeling while considering various competing accuracy or performance measures altogether.

A Comparative Review of AI Techniques for Automated Code Generation in Software Development: Advancements, Challenges, and Future Directions

Artificial Intelligence (AI), as one of the most important fields of computer science, plays a significant role in the software development life cycle process, especially in the implementation phase, where developers require considerable effort to convert software requirements and design into code. Automated Code Generation (ACG) using AI can help in this phase. Automating the code generation process is becoming increasingly popular as a solution to address various software development challenges and increase productivity. In this work, we provide a comprehensive review and discussion of traditional and AI techniques used for ACG, their challenges, and limitations. By analysing a selection of related studies, we will identify all AI methods and algorithms used for ACG, extracting the evaluation metrics and criteria such as Accuracy, Efficiency, Scalability, Correctness, Generalization, and more. These criteria will be used to perform a comparative result for AI methods used for ACG, exploring their applications, strengths, weaknesses, performance, and future applications.

Hybrid Framework To Exclude Similar and Faulty Test Cases In Regression Testing

Regression testing is a crucial phase in the software development lifecycle that makes sure that new changes/updates in the software system don’t introduce defects or don’t affect adversely the existing functionalities. However, as the software systems grow in complexity, the number of test cases in regression suite can become large which results into more testing time and resource consumption. In addition, the presence of redundant and faulty test cases may affect the efficiency of the regression testing process. Therefore, this paper presents a new Hybrid Framework to Exclude Similar & Faulty Test Cases in Regression Testing (ETCPM) that utilizes automated code analysis techniques and historical test execution data to identity and exclude redundant, similar and faulty test cases from the given regression suite. Our experimental results clearly show the benefits of the ETCPM framework in terms of reduction in the testing time, optimization of the resource allocation, and improvement in the overall quality of regression test suite. ETCPM enables software development teams to achieve faster and reliable regression testing by intelligent exclusion of similar and fault test cases, which yields in reduction in the software delivery cycles and better end user satisfaction.

Mitigating Software Vulnerabilities through Secure Software Development with a Policy-Driven Waterfall Model

For the past few years, software security has become a pressing issue that needs to be addressed during software development. In practice, software security is considered after the deployment of software rather than considered as an initial requirement. This delayed action leads to security vulnerabilities that can be catered for during the early stages of the software development life cycle (SDLC). To safeguard a software product from security vulnerabilities, security must be given equal importance with functional requirements during all phases of SDLC. In this paper, we propose a policy-driven waterfall model (PDWM) for secure software development describing key points related to security aspects in the software development process. The security requirements are the security policies that are considered during all phases of waterfall-based SDLC. A framework of PDWM is presented and applied to the e-travel scenario to ascertain its effectiveness. This scenario is a case of small to medium-sized software development project. The results of case study show that PDWM can identify 33% more security vulnerabilities as compared to other secure software development techniques.

Comprehensive Survey of Cutting-Edge Methods for Software Architecture Evaluation

The crucial responsibility of assessing software architecture is of utmost importance in ensuring that a software system conforms to superior qualities. It is a crucial tool for cutting expenses and labor during the course of the software development lifecycle. The main goal of software architecture evaluation is to provide reliable methods for determining and improving the quality characteristics that are innate in software. This study is deeply devoted to investigating the wide range of techniques used in software architecture assessment. One of the main focuses of these evaluation techniques is scenario-based assessments, which offer a comprehensive picture of the software's behavior under different circumstances. Within the scope of this study, a thorough analysis is carried out on eighteen different methods. Thirteen of these fall into the category of early evaluation techniques, which are positioned to detect and address architectural problems at the outset of development. The other five strategies are classified as late assessment techniques and focus on validating and optimizing the software architecture in the latter stages of the development process.

Soft Computing-Based System for Performance Modeling of Object-Oriented Programming (OOP) Software

Performance is a vital attribute for most software, making performance investigation an essential software engineering task. The problem is that modern applications are challenging to analyse for performance. The advancement of Software engineering has seen the application of scientific approach at various stages of Software Development Life Cycle (SDLC). New technique is available for implementation known as soft computing. This approach is a synergistic combination of artificial intelligence methodologies to model and solve real world problems that are either impossible or challenging to be modeled mathematically. The complexity, the commercial constraints and the expectation for high quality software demand, measuring the quality performance of an Object-Oriented Software (OOS). This work presents a set of testing metric (criteria) which hold the potential for ensuring remarkable improvements in quality performance of Object-Oriented software (OOS). A Prediction Model focused on identifying and evaluating the quality of the performance model based on the data set of software design components. The software design properties are comprehensive elucidated and integrating concepts of fuzzy logic and software engineering. The system enables the performance of modeling of Object-Oriented software using Fuzzy Inference System (FIS) to accurately predict the quality Object Oriented Software based on quality performance criteria such as reliability, efficiency, functionality and maintainability. Empirical validation of the results using checking data set is made to prove the usefulness of the design metrics and design quality attributes on the software prediction models. The purpose of this work is to provide development tool to solves practical problems, and focus on identifying and evaluating the quality performance of software design components. Again, the results demonstrate that, despite the research including approaches explicitly aimed at object-oriented software, there are substantial challenges in providing realistic feedback on the performance of large-scale object-oriented applications accordingly.

Design, Development and Validation of a Digital Performance Based Appraisal System: A Planned Approach in Change Management

Objectives: In India, the professional performance of the academic faculty is assessed annually using Academic Performance Indicator (API) as per the University Grants Commission (UGC), Government of India. This is a manual model is cumbersome in filling, and time-consuming. As a planned change and a measure to introduce technology-based platforms in JSS Academy of Higher Education & Research (JSSAHER), Mysuru, India, we have designed, developed, and validated digital platform technology to evaluate the annual performance of faculty working in medical, dental, pharmacy, life sciences fraternities. Methods: Academic Performance Indicator (API) guideline as per the University Grants Commission (UGC), Government of India, was used to design and develop the digital performance-based appraisal system (d-PBAS), and its performance was validated (20% random sampling) by comparing the manual and digital data entered by the faculty from different disciplines of JSSAHER. Findings: The present manuscript demonstrates the designing and validation methodology of d-PBAS. Software Development Life Cycle (SDLC) process and Waterfall model was adopted to develop d-PBAS. The digital platform was assessed for the performance and ranking of the faculty. More than 96% of the users expressed satisfaction and 4% expressed dissatisfaction in d-PBAS. Novelty: For the first of its kind in a university set up in India, JSSAHER designed and validated the digital performance-based appraisal system to evaluate academic performance. Since d-PBAS was developed using the SDLC process and Waterfall model it is easy and consumes less time to enter the data. The most important highlight is d-PBAS helps to rank the faculty performance automatically. The design and validation of d-PBAS described in the present manuscript are simple, robust, cost-effective, and have a go-green value. Keywords: Change Management, Higher Education Institutions, Performance­based, Appraisal System, academic Performance Indicator, digital technology

PERANCANGAN ABSENSI QR CODE MAHASISWA BERBASIS WEBSITE PADA STIKOM TUNAS BANGSA PEMATANG SIANTAR MENGGUNAKAN METODE AGILE

This research discusses the implementation of a web-based attendance system that utilizes QR scanner technology to record individual attendance, especially in an educational environment. The report also highlights the primary benefits of this approach, such as process efficiency, data accuracy, and real-time information access. The web-based attendance system using QR scanner technology not only enhances administrative effectiveness but also provides a more modern and interactive experience for individuals who need to be accounted for. This helps reduce the risk of errors in manual attendance recording and provides faster and more accurate access to attendance data. In this research, the author used various methods to collect data, including observation and a literature review. The author relied on various online documentation sources as data references for this report. The documents used by the author included several Scopus-indexed journals, e-books, and various digital news pages from the past 5 years. For the development method of this website, the author employed the Agile methodology. Agile is one of the models of the Software Development Life Cycle (SDLC). The results of this research, which combine QR codes and web access, make this attendance system a relevant and innovative solution for efficient attendance management.

Microservices and serverless functions—lifecycle, performance, and resource utilisation of edge based real-time IoT analytics

Edge Computing harnesses resources close to the data sources to reduce end-to-end latency and allow real-time process automation for verticals such as Smart City, Healthcare and Industry 4.0. Edge resources are limited when compared to traditional Cloud data centres; hence the choice of proper resource management strategies in this context becomes paramount. Microservice and Function as a Service architectures support modular and agile patterns, compared to a monolithic design, through lightweight containerisation, continuous integration/deployment and scaling. The advantages brought about by these technologies may initially seem obvious, but we argue that their usage at the Edge deserves a more in-depth evaluation. By analysing both the software development and deployment lifecycle, along with performance and resource utilisation, this paper explores microservices and two alternative types of serverless functions to build edge real-time IoT analytics. In the experiments comparing these technologies, microservices generally exhibit slightly better end-to-end processing latency and resource utilisation than serverless functions. One of the serverless functions and the microservices excel at handling larger data streams with auto-scaling. Whilst serverless functions natively offer this feature, the choice of container orchestration framework may determine its availability for microservices. The other serverless function, while supporting a simpler lifecycle, is more suitable for low-invocation scenarios and faces challenges with parallel requests and inherent overhead, making it less suitable for real-time processing in demanding IoT settings.

Practitioners' expectations on automated release note generation techniques

AbstractThe software development life cycle relies heavily on the software release note, a crucial document. Various practitioners, including project managers and clients, benefit from release notes as they provide an overview of the latest software release. However, the manual generation of release notes is a time‐consuming and stressful task. Researchers have recently proposed automated techniques to generate release notes, saving developers' time and enhancing their understanding of software projects. Unfortunately, the adoption of these tools in practice remains limited. To address this gap, we have taken steps to understand the expectations and requirements of practitioners regarding release note generation techniques before implementing new automated approaches. Consequently, our approach involves two main stages: First, we conduct a comprehensive review of the relevant literature and analyze existing release notes from GitHub repositories to gain insights into the current practices. Second, we conduct an online survey study to gather input from practitioners and understand their expectations regarding release notes. We have reviewed 16 papers related to release notes and explored 3347 release notes from 21 GitHub repositories. Our analysis revealed key artifacts present in release note contents, including issues (29%), pull requests (32%), commits (19%), and common vulnerabilities and exposures (CVE) issues (6%). Additionally, we conducted a survey study involving 32 professionals to understand the essential information that should be included in release notes based on users' roles. For instance, project managers were more interested in learning about new features rather than less critical bug fixes. Furthermore, we identified gaps in existing systems and essential factors to consider when implementing release notes techniques in software engineering. The insights gained from our study can guide future research directions and assist practitioners in generating release notes with relevant content, thus improving the overall quality of documentation in software development.

Water Meter Reading Application System Development using Image Processing A Case Study from Sindangsari Village Water Services

PAMDES is a drinking water company managed by local villagers. The water meter data are read and recorded manually without any technology, which is ineffective and inefficient. Digital image processing can be implemented to read and record the water meter data automatically. When implemented in the water meter, it can help PAMDES officers to read the data without the internet, without changing the conventional water meter device, and the water meter data can be read and recorded effectively and efficiently. This research used the agile method, one of the methods used in the Software Development Life Cycle (SDLC). The method is done repetitively within a short period of time. The output of this research is an application with a digital image processing model that can read water meter data up to 82% in normal conditions and still can be improved. This research aims to make the water meter data reading and recording more effective and efficient and to contribute to the transformation of Sindangsari village into a digitalized village.

Exploration of Problems and Key Points in Database Design in Software Development

Starting from the necessity and principles of database design, this article explores the optimization issues. Firstly, analyze the necessity of database design, elaborating on effective management, maintainability, resource utilization, and running speed; Then, a series of issues in database management were discussed, such as user management, data object design specifications, and overall design ideas; Finally, the optimization issues such as normalization rules, inter table redundancy handling, query optimization, indexing, and transactions were elaborated in detail. In the software development lifecycle, database design is indispensable. Its role is not only to ensure the safety and reliability of data, but also to ensure the overall stability and speed of the system. Strengthening the rationality and optimization of design is the key to improving software quality.