Efficiency Of Software Development Research Articles

The Impact of Teamwork Quality (TWQ) on Agile Software Development Team Performance

Purpose: The study examined the extent to which teamwork quality (TWQ) impacts team performance in the Agile software development space in the U.S. and how the TWQ factors (Communication, coordination of expertise, cohesion, trust, mutual support & value sharing) are ranked in terms of importance to team performance Materials and Methods: The study used correlation and regression analysis to determine the degree of the relationship and the effect of the extended TWQ factors on Agile software team performance. Additionally, the study assessed the relative importance of the TWQ factors to team performance. Participants in the study included IT workers from the software development team in U.S. companies with in-house teams using the Agile methodology. Data was collected using an online survey through survey monkey. The data was presented through descriptive statistics and analyzed using regression analysis. Findings: The study found all the extended TWQ factors were strongly correlated to the team performance. The analysis showed that a statistically significance predictive relationship exists between the extended TWQ factors and Agile software development team performance. However, only mutual support and value sharing contributed statistically significantly to the model. Among these factors, mutual support was ranked as the most important TWQ factor in relation to team performance, while trust was ranked as the least. Implications to Theory, Practice and Policy: This empirical study tested and extended the TWQ model on team performance in the Agile software development space in the U.S. for software development teams and how these TWQ factors are ranked in terms of importance to team performance. The findings provide insight to project teams and team leaders, product owners, scrum masters, and project managers about the TWQ model from the perspective of agile software development teams. The findings also address the critical TWQ factors that are needed for building effective and efficient Agile software development teams.

Overview of software testing

Software testing is a task conducted to assess the quality of software and enhance its performance. This process ensures that the software behaves as expected according to user requirements. The software development life cycle (SDLC) outlines various stages such as Analysis, Requirements, Design, Development, Testing, Deployment, and Maintenance to guide the software development process. The primary objective of SDLC is to deliver error-free software that meets user expectations within specified timeframes. Software testing is an integral part of SDLC, serving to identify issues and ensure the completeness, correctness, and quality of the developed software. The ultimate goal is to create efficient software and maintain high-quality assurance throughout the product's lifecycle. This review paper aims to provide a brief discussion on how the software development life cycle contributes to efficient software development and how the software testing life cycle process ensures better quality assurance for the product.

Research on the Application of Intelligent Multi-branch Version Management Strategy of Electricity Marketing Service System Software

In recent years, the Market Marketing Department of State Grid Corporation of China, in collaboration with the Internet Department, has initiated the construction of an intelligent energy internet marketing service system. This paper aims to improve the development efficiency and quality of the power marketing service system. To achieve this goal, an intelligent multi-branch version management strategy has been introduced. Firstly, the necessity of multi-branch version management is analyzed in relation to the characteristics of the power marketing service system software and the potential issues that may arise in its development and version management process. Then, based on the concept of productizing the general functions of the entire network and projecting the functions of each provincial network, a multi-branch version management strategy is designed, which includes the core components of intelligent multi-branch version management and intelligent multi-branch version protection. Finally, an application case of power marketing service system development is presented. This solution not only standardizes the version management process, improves software development efficiency, ensures the order and control of versions, but also demonstrates the enormous potential of intelligent technology in enhancing system development and management efficiency.

From programming-to-modeling-to-prompts smart ubiquitous applications

Since its introduction by Mark Weiser, ubiquitous computing has received increased interest in the dawn of technological advancement. Supported by wireless technology advancement, embedded systems, miniaturization, and the integration of various intelligent and communicative devise, context-aware ubiquitous applications actively and intelligently use rich contextual information to assist their users. However, their designs are subject to continuous changes imposed by external factors. Nowadays, software engineering, particularly in the fields of Model-Driven Engineering, displays a strong tendency towards developing applications for pervasive computing. This trend is also fueled by the rise of generative artificial intelligence, paving the way for a new generation of no-code development tools and models specifically trained on open-source code repositories to generate applications from their descriptions. The specificities of our approach lies in starting with a graphical model expressed using a domain-specific language (DSL) composed of symbols and formal notations. This allows for graphically instantiating and editing applications, guiding and assisting experts from various engineering fields in defining ubiquitous applications that are eventually transformed into peculiar models. We believe that creating intelligent models is the best way to promote software development efficiency. We have used and evaluated recurrent neural networks, leveraging the recurrence of processing the same contextual information collected within this model, and enabling iterative adaptation to future evolutions in ubiquitous systems. We propose a prototype instantiated by our meta-model which tracks the movements of individuals who were positive for COVID-19 and confirmed to be contagious. Different deep learning models and classical machine learning techniques are considered and compared for the task of detection/classification of COVID-19. Results obtained from all techniques were evaluated with confusion matrices, accuracy, precision, recall and F1-score. In summary, most of the results are very impressive. Our deep learning approach used a RNN architecture produced up to 92.1% accuracy. With the recent development of OpenAI Codex, optimized for programming languages, we provided the same requirements to the Codex model and asked it to generate the source code for the COVID-19 application, comparing it with the application generated by our workshop.

Richen: Automated enrichment of Git documentation with usage examples and scenarios

AbstractAs the predominant modern version control system, Git has become an indispensable tool for both commercial and open‐source software projects. It substantially improves software development effectiveness and efficiency through its distributed version control system, fostering seamless collaboration among teams and across locations. However, research has found that many developers have doubts about using Git commands, while the official Git documentation is rather scanty, that is, lacking sufficient explanations and examples. To help developers learn and use Git commands, we propose the first approach (Richen) for enriching Git documentation with usage examples and scenarios by leveraging crowd knowledge from Stack Overflow. Richen retrieves Git‐related posts from Stack Overflow, extracts relevant Q&A pairs, and selects representative command usages, including usage examples and scenarios, for different Git commands. Experimental results have shown that Richen can extract informative and concise command usages for Git commands. Compared with alternative methods adapted from API usage mining, the command usages obtained by Richen have significant advantages in terms of relevance, readability, and usability. Furthermore, we have shown through an empirical study that the command usages extracted by Richen can better help developers complete Git command‐related tasks.

Deep code search efficiency based on clustering

AbstractThe deep‐learning based code search model mainly takes accuracy as the only target for judging the performance of the model, ignoring the efficiency of code search. This article proposes a clustering‐based code search model (C‐DCS). C‐DCS uses the K‐Means to divide the code vector base into K clusters and obtains the center vectors of K clusters. While searching, C‐DCS first matches the query vector with the K center vectors to get the best matching center vector. After matching the center vector, C‐DCS matches the query vector with code vectors in the cluster corresponding to the best matching center vector one by one and then gets the best matching code snippet vector. To verify the efficiency of C‐DCS in the code search task, experimental analysis was built on a large dataset. The experimental results showed that C‐DCS saves 92.2% of the search time compared to the baseline model while remaining the accuracy. In the experimental evaluation section, we optimized the K‐Means algorithm to improve the code search efficiency of C‐DCS further, reducing the search time to 93.8% of the baseline model. Hence, C‐DCS reduces the code search time greatly with not affecting the accuracy, improving the efficiency of software development.

The Role of object-oriented Programming in sustainable and Scalable Software Development

This study empirically evaluates how various practices and principles of object-oriented programming (OOP) impact the sustainability and scalability of software projects. A detailed analysis of real-world projects is carried out, considering aspects such as encapsulation, code reuse, modularity, and the application of inheritance and polymorphism. The goal was to identify patterns and best practices contributing to efficient and adaptable software development. The main results highlight that the adoption of OOP practices, such as encapsulation, code reuse, modularity, inheritance, and polymorphism, can be essential for efficient and adaptable software development over time, effectively addressing scalability and sustainability challenges in real-world projects.

Unlocking the Potential of AI/ML in DevSecOps: Effective Strategies and Optimal Practices

In the dynamic realm of technology, the fusion of Artificial Intelligence (AI) and Machine Learning (ML) with DevSecOps practices stands out as a pivotal catalyst for bolstering security, efficiency, and innovation in software development and deployment processes. This document explores effective strategies and optimal practices for maximizing the capabilities of AI/ML within the DevSecOps framework. Commencing with an overview of DevSecOps principles and the integral role of AI/ML, the document delves into specific tactics such as automated threat detection, predictive analytics for vulnerability management, and intelligent automation for continuous integration and deployment. Additionally, it addresses prominent challenges and considerations associated with the integration of AI/ML in DevSecOps, including data privacy, algorithm transparency, and ethical implications. Through illuminating case studies and real-world illustrations, the document showcases how organizations can leverage AI/ML technologies to streamline their DevSecOps pipelines, mitigate security risks, and cultivate a culture of ongoing enhancement. By embracing these strategies and adhering to best practices, organizations can harness the full potential of AI/ML to propel innovation, fortify resilience, and enhance agility in their DevSecOps endeavors.

Unlocking the Potential of AI/ML in DevSecOps: Effective Strategies and Optimal Practices

In the dynamic realm of technology, the fusion of Artificial Intelligence (AI) and Machine Learning (ML) with DevSecOps practices stands out as a pivotal catalyst for bolstering security, efficiency, and innovation in software development and deployment processes. This document explores effective strategies and optimal practices for maximizing the capabilities of AI/ML within the DevSecOps framework. Commencing with an overview of DevSecOps principles and the integral role of AI/ML, the document delves into specific tactics such as automated threat detection, predictive analytics for vulnerability management, and intelligent automation for continuous integration and deployment. Additionally, it addresses prominent challenges and considerations associated with the integration of AI/ML in DevSecOps, including data privacy, algorithm transparency, and ethical implications. Through illuminating case studies and real-world illustrations, the document showcases how organizations can leverage AI/ML technologies to streamline their DevSecOps pipelines, mitigate security risks, and cultivate a culture of ongoing enhancement. By embracing these strategies and adhering to best practices, organizations can harness the full potential of AI/ML to propel innovation, fortify resilience, and enhance agility in their DevSecOps endeavors.

Efficient Software Development Process Using Component-Based and ORM

The software development process has many variations. Each software development technique has its own advantages and disadvantages. There are efficient development processes and some could be more efficient. This research aims to build a web-based application efficiently utilizing component-based and ORM with a case study of financial management applications on community radio. This research begins with data collection through observations and interviews, requirement analysis, and system design, implementation, and testing. This research shows that combining component-based software development and object-relational mapping (ORM) can increase software development efficiency compared to non-component-based and pure SQL.

Enhanced model-driven web application development with code generation using deep learning technique

Advancements in modern technology have led to an endless reliance on the Internet. This has created a great demand for the fast and accurate development of web applications. Web development has benefitted from programming approaches such as Model-Driven Web Engineering (MDWE). MDWE allows developers to choose pre-defined models and utilize them for their requirements. This kind of structural blueprinting, called wireframing, enables efficiency in software development. However, these techniques are seldom understood by people without a technological background. Hence, much of the coding central to a project remains the responsibility of a few tech-educated people. This work proposes an approach that offloads some of the typing to a machine-based code generator. This has been achieved by pairing MDWE methods with Deep Learning capabilities. This ensures a less coding-intensive web development methodology that can be utilized even by non-web developers. This work makes several contributions to improving overall MDWE methods.

An Empirical Study of the Code Generation of Safety-Critical Software Using LLMs

In the digital era of increasing software complexity, improving the development efficiency of safety-critical software is a challenging task faced by academia and industry in domains such as nuclear energy, aviation, the automotive industry, and rail transportation. Recently, people have been excited about using pre-trained large language models (LLMs) such as ChatGPT and GPT-4 to generate code. Professionals in the safety-critical software field are intrigued by the code generation capabilities of LLMs. However, there is currently a lack of systematic case studies in this area. Aiming at the need for automated code generation in safety-critical domains such as nuclear energy and the automotive industry, this paper conducts a case study on generating safety-critical software code using GPT-4 as the tool. Practical engineering cases from the industrial domain are employed. We explore different approaches, including code generation based on overall requirements, specific requirements, and augmented prompts. We propose a novel prompt engineering method called Prompt-FDC that integrates basic functional requirements, domain feature generalization, and domain constraints. This method improves code completeness from achieving 30% functions to 100% functions, increases the code comment rate to 26.3%, and yields better results in terms of code compliance, readability, and maintainability. The code generation approach based on LLMs also introduces a new software development process and V-model lifecycle for safety-critical software. Through systematic case studies, we demonstrate that, with appropriate prompt methods, LLMs can auto-generate safety-critical software code that meets practical engineering application requirements. It is foreseeable that LLMs can be applied to various engineering domains to improve software safety and development efficiency.

Enhancing Software Quality through Early-Phase of Software Verification and Validation Techniques

Purpose: The core objectives of this conducted research were threefold: first, to assess whether early-phase verification and validation (V&V)techniques, encompassing software design review, code review, and inspection, significantly contribute to defect prevention throughout the software development process; second, to evaluate the impact of these techniques on the efficiency of software development in terms of time, cost, and resource allocation; and third, to conduct a comprehensive comparative analysis between early-phase verification and validation (V&V)techniques and traditional post-development testing regarding their effectiveness in defect prevention and software quality improvement.
 Methodology: Employing an experimental approach, this study conducted case studies within Tanzanian software development organizations. Data were meticulously gathered through surveys and interviews involving software professionals. Both quantitative and qualitative data were systematically analyzed to ensure data reliability and ethical considerations.
 Findings: The research findings indicate that early-phase verification and validation (V&V) techniques shine as powerful tools for defect prevention, leading to a substantial reduction in defect counts and elevating software quality metrics, such as reliability and user satisfaction. Notably, these techniques demonstrated a favorable impact on software development efficiency by shortening development cycles and curtailing costs. When compared to traditional post-development testing, early-phase verification and validation (V&V) techniques consistently outperformed in the realm of defect prevention and software quality enhancement.
 Unique Contribution to Theory, Practice and Policy: The study's outcomes underscore the transformative potential of early-phase verification &validation techniques, urging the software development industry to pivot from defect detection to defect prevention. The insights garnered here offer practical guidance for organizations aiming to streamline their software development processes, economize costs, and deliver top-notch software products that closely align with user expectations. While conducted in a Tanzanian context, these findings reverberate across the global software development landscape, underscoring the universal relevance of early-phase verification and validation (V&V) techniques as pivotal enablers of software quality assurance and operational efficiency.

Code Clone Detection Based on Bytecode and Twin Neural Networks

Abstract In order to perform code clone detection in missing source code scenarios while ensuring the code clone detection effect, this paper proposes a code clone detection method based on bytecode and twin neural networks. The process begins by extracting the function’s opcode sequence from the bytecode instruction file. Then, the opcodes are vectorized using a neural network pre-training model to ensure that they contain semantic information. Then, a twin neural network is constructed based on GRU to compute the similarity between the vector sequences. The Opcode21K dataset dedicated to bytecode is used to test the constructed model. A total of 5818611 real clone pairs and 279112 fake clone pairs are detected, and the clone pairs that have been labeled by Opcode21K are plotted on the ROC curve so as to select the distance value of 0.7 as the code clone detection threshold. The number of clone pairs detected by SJBCD, the accuracy, and the recall rate are much higher than those of most existing methods. The number of large-difference code clones detected ranges from about 20% to 50% of the total clones. Additionally, the method’s runtime is the shortest for datasets with code lines ranging from 1M to 30M in size, and the detection time for a 250M dataset is approximately 54.5 hours. Therefore, the algorithm constructed in this study can take into account the detection of code clones in a variety of situations so that the efficiency of software development can be effectively improved.

The Importance of Robust Communication in Large-Scale Agile Development

The agile methodology stands out as a prevalent model for efficient software development, particularly favored for its adaptability and suitability in small-scale projects across various software industries. Nevertheless, its widespread adoption has brought to light certain communication challenges, particularly when applied to large-scale distributed teams. Agile, it appears, may not be the optimal choice for extensive teams engaged in global software development efforts. This study delves into the intricacies of issues faced by teams employing agile in the context of large-scale distributed development, particularly focusing on communication-related challenges and their repercussions. Our approach involved in-depth interviews with diverse developers and teams hailing from various sectors within the software industry. Moreover, we conducted an extensive quantitative analysis, surveying 50 developers representing different distributed teams. The outcomes of our investigation unearthed several communication-related deficiencies that significantly impact the development process. To arrive at these insights, we employed two robust statistical analysis methods: descriptive analysis and regression analysis. The implications of our findings have led us to propose innovative software solutions, bearing distinctive features engineered to mitigate the communication issues often encountered in large-scale software development. These solutions have the potential to enhance the efficiency and effectiveness of agile practices when applied in extensive and globally dispersed development endeavors.

Estimating Uncertainty in Labeled Changes by SZZ Tools on Just-In-Time Defect Prediction

The aim of Just-In-Time (JIT) defect prediction is to predict software changes that are prone to defects in a project in a timely manner, thereby improving the efficiency of software development and ensuring software quality. Identifying changes that introduce bugs is a critical task in just-in-time defect prediction, and researchers have introduced the SZZ approach and its variants to label these changes. However, it has been shown that different SZZ algorithms introduce noise to the dataset to a certain extent, which may reduce the predictive performance of the model. To address this limitation, we propose the Confident Learning Imbalance (CLI) model. The model identifies and excludes samples whose labels may be corrupted by estimating the joint distribution of noisy labels and true labels, and mitigates the impact of noisy data on the performance of the prediction model. The CLI consists of two components: identifying noisy data (Confident Learning Component) and generating a predicted probability matrix for imbalanced data (Imbalanced Data Probabilistic Prediction Component). The IDPP component generates precise predicted probabilities for each instance in the training set, while the CL component uses the generated predicted probability matrix and noise labels to clean up the noise and build a classification model. We evaluate the performance of our model through extensive experiments on a total of 126,526 changes from ten Apache open source projects, and the results show that our model outperforms the baseline methods.

Formula omitted]: A simplified and abstract multicore hardware model for large scale system software formal verification

This paper introduces SimplMM, a novel subsystem within the Certified Concurrent Abstraction Layers (CCAL) modular software verification framework, designed specifically for fine-grained concurrent software. SimplMM aims to provide a generic, practical, and realistic multicore machine model for verifying software within the CCAL framework.While formal multicore hardware semantics have seen extensive development, their integration with large-scale software verification has received limited attention. To address this gap, we propose a novel approach: a toolkit comprising a generic sequentially consistent multicore semantics, contextual refinement templates, and libraries. These components establish crucial connections between the machine model and verified program modules (layers) using CCAL. We demonstrate the practicality of our framework by successfully integrating it with existing large-scale proofs, specifically for CertiKOS running on top of the x86 hardware architecture.This research significantly advances the field of accurate and efficient concurrent software verification and development tools for multicore systems. Our provision of a practical and formal multicore machine model, seamlessly integrated within the CCAL framework, equips developers with a powerful toolkit for large-scale concurrent software verification. The effectiveness of our approach, validated through successful integration with existing large-scale proofs such as CertiKOS, establishes a robust foundation for the design and verification of concurrent software in multicore systems.

Multi-Project Multi-Environment Approach—An Enhancement to Existing DevOps and Continuous Integration and Continuous Deployment Tools

DevOps methodology and tools, which provide standardized ways for continuous integration (CI) and continuous deployment (CD), are invaluable for efficient software development. Current DevOps solutions, however, lack a useful functionality: they do not support simultaneous project developments and deployment on the same operating infrastructure (e.g., a cluster of Docker containers). In this paper, we propose a novel approach to address this shortcoming by defining a multi-project, multi-environment (MPME) approach. With this approach, a large company can organize many microservice-based projects operating simultaneously on a common code base, using self-hosted Kubernetes clusters, which helps developers and businesses to better focus on the product they are developing, and to reduce efforts on the management of their DevOps infrastructure.

Towards Efficient Robotic Software Development by Reusing Behavior Tree Structures for Task Planning Paradigms

Towards Efficient Robotic Software Development by Reusing Behavior Tree Structures for Task Planning Paradigms

CCCS: Contrastive Cross-Language Code Search Using Code Graph Information

Developers often search and reuse existing code snippets to improve software development efficiency during software development. Currently, researchers have proposed many code search methods. However, the search intent of existing methods is basically a natural language query. In order to support code migration and code refactoring, it is necessary to search relevant code snippets of another programming language with code snippets of one programming language. In this paper, we propose a C ontrastive C ross-language C ode S earch method using code graph information, called CCCS . CCCS first converts code snippets into high-dimensional vectors using pre-trained CodeBERT to extract the sequence features of code snippets. Next, the structural features of code snippets are extracted using a graph convolutional neural network. Finally, the model is trained using the contrastive learning method to optimize the vector representation of cross-language code snippets, enabling the model to distinguish code snippets from different programming languages with the same functionality. To evaluate the effectiveness of our method, we conducted comparison experiments and ablation experiments on a small-scale dataset and a large-scale dataset, respectively. The experimental results show that our method far outperforms the state-of-the-art baseline model in terms of MRR metrics.