Quality Of Software Development Research Articles

Generative Models for Source Code: Fine-Tuning Techniques for Structured Pattern Learning

This study addresses the problem of how to automatically generate source code that is not only functional, but also well-structured, readable, and maintainable. Existing generative models for source code often produce functional code, but they lack consistency in structure and adherence to coding standards, essential for integration into existing application development projects and long-term software maintenance. By training the model on specific code structures, including a dataset with Italian annotations, the proposed methodology ensures that the generated code is compliant with both the functional requirements and the pre-defined coding standards. The methodology proposed in this study applies transfer learning techniques on the DeepSeek Coder model, to refine pre-trained models to generate code that integrates additional structuring constraints. By training the model on specific code structures, including a dataset with Italian comments, the proposed methodology ensures that the generated code meets both functional requirements and coding structure. Experimental results, evaluated using the perplexity metric, demonstrate the effectiveness of the proposed approach, which impacts the goals of reducing errors, and ultimately improves software development quality.

Improving bioinformatics software quality through teamwork.

Since high-throughput techniques became a staple in biological science laboratories, computational algorithms, and scientific software have boomed. However, the development of bioinformatics software usually lacks software development quality standards. The resulting software code is hard to test, reuse, and maintain. We believe that the root of inefficiency in implementing the best software development practices in academic settings is the individualistic approach, which has traditionally been the norm for recognizing scientific achievements and, by extension, for developing specialized software. Software development is a collective effort in most software-heavy endeavors. Indeed, the literature suggests teamwork directly impacts code quality through knowledge sharing, collective software development, and established coding standards. In our computational biology research groups, we sustainably involve all group members in learning, sharing, and discussing software development while maintaining the personal ownership of research projects and related software products. We found that group members involved in this endeavor improved their coding skills, became more efficient bioinformaticians, and obtained detailed knowledge about their peers' work, triggering new collaborative projects. We strongly advocate for improving software development culture within bioinformatics through collective effort in computational biology groups or institutes with three or more bioinformaticians.

A Systematic Literature Review on the Influence of Enhanced Developer Experience on Developers' Productivity: Factors, Practices, and Recommendations

Context and Motivation – Developer eXperience (Dev-X) is a recent research area that focuses on developers perceptions, feelings, and values with respect to software development and software quality. Research suggests that factors and practices related to Dev-X can have a substantial impact on developer productivity (Dev-P). However, despite a large and diverse body of literature on factors that can impact Dev-P in general, there is no coherent and comprehensive characterization of how Dev-X-specific insights can influence developer productivity. Aims – In the presented research, we aim to provide a coherent, comprehensive characterization of factors and practices related to Dev-X, with a particular focus on those factors and practices that potentially affect Dev-P. Method – To this end, we performed a systematic literature review and identified 218 relevant papers in this area. We characterize the papers based on the related frameworks and concepts common to Dev-X and Dev-P as presented in the existing literature. Dev-X factors such as “work fragmentation” and practices such as “collaboration with owner-developer” are identified using a grounded-in-the-literature, content-analysis method, guided by the theory. For each Dev-X factor, we identify attributes that might be used to assess/ measure the current status (of an organization or project) regarding that factor and how that factor and its effects on productivity have been evidenced in the literature (mentioned vs. considered in questionnaires vs. substantiated with a more positivist evaluation). Results – We identify 33 Dev-X-related factors and 41 Dev-X-related practices, which are organized into 10 themes to summarize their influence. The results suggest that the availability of required resources, relevant expertise re the allocated tasks, and fewer interruptions are among the top positively impacting factors. Conversely, factors such as code complexity, heterogeneous contexts of tasks, and non-adherence to standardization harm Dev-X and Dev-P. Top industrial practices employed to mitigate the negative influence of factors include characterization-based task assignments, mental model support, and the timely evolution of technologies. Conclusions – Overall, this research suggests that organizations can influence Dev-P through improved Dev-X, incorporating suitable practices to mediate relevant factors in their context. Important in this regard are practices such as fragmenting large tasks, highlighting the utility of proposed tasks/changes to the developers, and promoting (developer) ownership of artefacts. Finally, our results point to areas where further research seems appropriate, i.e., where Dev-X factors/practices have been proposed as being impactful on Dev-P but not yet fully substantiated or explored as such (factors like “Nature of Activity” and practices like choosing practices/protocols appropriately).

Demystifying The Object-Oriented Features Of Popular Object-Oriented Programming Languages.

Background: Indeed, Object-Oriented Programming (OOP) has emerged as one of the major fundamental paradigms in today’s world of software development as it contains principles that enhance modularity, reusability, and maintainability of software. This article explores deeper into some of the facets of OOP aspects as exhibited in some of the most frequently used programming languages ranging from Java, Python, C++, and C# with an intent of offering substantive information to aspects like encapsulation, inheritance, polymorphism, and abstraction. The article synthesizes the ones focusing on the relevance and conceptualization of OOP and problems related to it in the context of learners and practitioners. It also describes strengths and weaknesses of using C++, Python, and Java in relation to OOP while stressing peculiarity of these languages. Some of the core principles of OOP are highlighted such as encapsulation, inheritance, polymorphism, and abstraction and how they are complicated and significant in today’s software engineering. Materials and Methods: There was a presentation of a survey on which the research is based to determine the problems that programmers encounter when applying OOP features using a sample of 80 programmers in the computer science fields in some Nigerian Universities. Results: Findings reveal the significance of understanding OOP concepts in popular languages and suggest ways of increasing developers’ efficiency, minimizing errors, and improving the quality of developed software. Conclusion: Suggestions include a necessity for training and education to enhance software developers' understanding of OOP features. The findings can guide the creation of training materials and courses centered on OOP concepts, aiming to improve software development practices and elevate the quality of software products.

Discussion on the Innovation Path of Computer Software Engineering in the New Period

In the current era of digital transformation and rapid development of technology, computer software engineering is facing unprecedented opportunities and challenges. This paper discusses the importance of the innovation path of computer software engineering in the new era and its far-reaching influence on the industry and society. By analyzing the drivers of innovation and major challenges of software engineering innovation, this paper proposes four key strategies. These strategies contribute to the efficiency and quality of software development and also facilitate integrated innovation across disciplines and fields. This paper aims to explore the innovative path of computer software engineering in the new era, in order to help the related path.

Information and Communication Tools for Effective Functioning of Distributed Project Teams

The paper analyzes the key aspects of the use of Agile methodologies by distributed project teams in the IT industry with an emphasis on the integration of information and communication systems for effective work in dynamic environments. The challenges faced by the virtual IT industry in the context of the COVID pandemic, the large-scale invasion of Russia, which generated radical changes in the approaches to the management of project teams, have been considered. The considerable attention is paid to the role of video conferencing and other digital tools, which have become basic in ensuring uninterrupted work and effective interaction in distributed project teams, during global crises. An analysis of the main stack of information and communication technologies in distributed project environments indicates the widespread use of such platforms as Zoom, Microsoft Teams, Google Meet, Skype, Slack, Trello, Jira, and others to ensure flexibility, adaptability and high productivity of teams. The importance of synergy between the Agile methodology and its derivatives and digital tools is highlighted, demonstrating how it helps distributed IT project teams to adapt to changes, ensure high quality software development and foster effective communities of like-minded people. The paper provides information on the use of Agile methodology in 2023, which indicates the growth in the number of distributed project teams and captures the key factors for their successful functioning. It focuses on the importance of the integration processes of modern information and communication technologies and adaptive management practices in the dynamic world of IT projects, making a valuable contribution to the understanding of how distributed project teams can maintain a high level of productivity and efficiency, overcoming systemic obstacles and challenges that are formed in poorly predicted project environments.

FSDP: Frequent Software Defects Prediction Based on Defect Correlation Learning for Quality Software Development

FSDP: Frequent Software Defects Prediction Based on Defect Correlation Learning for Quality Software Development

A Survey of Source Code Search: A 3-Dimensional Perspective

(Source) code search is widely concerned by software engineering researchers because it can improve the productivity and quality of software development. Given a functionality requirement usually described in a natural language sentence, a code search system can retrieve code snippets that satisfy the requirement from a large-scale code corpus, e.g., GitHub. To realize effective and efficient code search, many techniques have been proposed successively. These techniques improve code search performance mainly by optimizing three core components, including query understanding component, code understanding component, and query-code matching component. In this paper, we provide a 3-dimensional perspective survey for code search. Specifically, we categorize existing code search studies into query-end optimization techniques, code-end optimization techniques, and match-end optimization techniques according to the specific components they optimize. These optimization techniques are proposed to enhance the performance of specific components, and thus the overall performance of code search. Considering that each end can be optimized independently and contributes to the code search performance, we treat each end as a dimension. Therefore, this survey is 3-dimensional in nature, and it provides a comprehensive summary of each dimension in detail. To understand the research trends of the three dimensions in existing code search studies, we systematically review 68 relevant literatures. Different from existing code search surveys that only focus on the query end or code end or introduce various aspects shallowly (including codebase, evaluation metrics, modeling technique, etc.), our survey provides a more nuanced analysis and review of the evolution and development of the underlying techniques used in the three ends. Based on a systematic review and summary of existing work, we outline several open challenges and opportunities at the three ends that remain to be addressed in future work.

PEPS: Polygenic Epistatic Phenotype Simulation.

Genetic data is limited and generating new datasets is often an expensive, time-consuming process, involving countless moving parts to genotype and phenotype individuals. While sharing data is beneficial for quality control and software development, privacy and security are of utmost importance. Generating synthetic data is a practical solution to mitigate the cost, time and sensitivities that hamper developers and researchers in producing and validating novel biotechnological solutions to data intensive problems. Existing methods focus on mutation frequencies at specific loci while ignoring epistatic interactions. Alternatively, programs that do consider epistasis are limited to two-way interactions or apply genomic constraints that make synthetic data generation arduous or computationally intensive. To solve this, we developed Polygenic Epistatic Phenotype Simulator (PEPS). Our tool is a probabilistic model that can generate synthetic phenotypes with a controllable level of complexity.

Impact of demographic attributes on requirement elicitation and quality software development

Impact of demographic attributes on requirement elicitation and quality software development

Impact of demographic attributes on requirement elicitation and quality software development

Impact of demographic attributes on requirement elicitation and quality software development

Research on Aerospace Software Quality Improvement Based on Software Test Data Analysis

This paper addresses the issue of underutilized software test data in current aerospace software quality management. Through analyzing the current third-party software evaluation process, the paper proposes to optimize software evaluation process and management to enhance software development quality and software test quality. The paper proposes an aerospace software quality improvement framework based on software test data analysis, which provides a reference scheme for aerospace software quality management. Overall, the paper emphasizes the importance of software test data analysis in improving aerospace software quality and provides a comprehensive solution for addressing problems identified in aerospace software quality management.

Enterprise architecture contribution in distributed agile software development

AbstractGeographically distributed agile development may experience a high failure rate due to communication issues, which has a negative influence on project performance. One suggested solution in the literature is to enhance both communication and project performance by implementing agile enterprise architecture. However, the empirical evidence that supports this claim is still scarce. To address this gap, this study empirically explores the role of agile enterprise architecture as an artifact in distributed agile development. The findings of an in‐depth qualitative case study from a dispersed agile development organization that involves teams distributed over three locations are used in this work. Over 2 months, data was gathered by interviewing 12 key members of the team and watching three Sprint sessions of agile software development. Text analysis qualitative approach was used to analyze the data. The findings imply that agile enterprise architecture has a positive effect on distributed agile software development communication, quality, and functionality. Agile enterprise architecture may also support on‐time completion, but a trade‐off with on‐budget may be necessary. These findings provide valuable insights, frameworks, and best practices that support organizations in achieving greater agility, collaboration, and success in their distributed software development initiatives. As this is one of the first studies to look at the influence of agile enterprise architecture on distributed agile software development communication and performance, further research is needed to confirm and expand on the conclusions of this study.

WEBSITE DESIGN OF BATAK TYPICAL DANCE PACKAGE DALIHAN NATOLU SIGAPITON STUDIO USING WEB ENGINEERING METHODS

Dalihan Natolu Sigapiton Studio is an art studio located in Sigapiton Village. This studio is one of the efforts to preserve the original village culture with native village teachers to make this dance pure, original that has been passed down from generation to generation. Many want to learn the original Batak dance which is known as the tor-tor dance from Sigapiton Village, however, the long distance makes it difficult for consumers to learn directly from the Dalihan Art Studio Natolu Sigapiton. The current problem is that there is no website or system that can help outsiders to learn traditional village dances. So it can be concluded that there are many wishes from the outside community to learn typical village dances, distance does not prevent them from learning. Based on previous research by Silvianti, Kiky Rizky Nova Wardani [1] entitled “UI Design UX Batak Dance Package at Website-Based Studio Dalihan Natolu Sigapiton Using the Design Thinking Method” has created a website design for Studio Dalihan Natolu Sigapiton. In this study, the aim was to design and build based on previous research by creating a website and adding features to become a Batak Dance Package Website for Dalihan Natolu Siapiton Studio Using the Web Engineering Method, which is a quality software development without spending a lot of money, based on the idea and the needs of the users.

Application of Meta-Programming Techniques for Accelerating Software Development and Improving Quality

A contemporary software tool has been devised to evaluate software quality through metric analysis techniques. This tool calculates pertinent metrics utilizing quality indicators and establishes a composite quality indicator value for software products. The intricacies of software quality assessment processes have been elucidated, including the examination of software quality's standardization as well as the presentation level of its model. This enables the potential for enhancement through the formulation of suitable criteria for quality assessment, refining models for metric analysis, and quantitatively measuring quality across all phases of project implementation. Notably, the use of metric analysis to gauge software quality reveals a lack of standardized metrics, resulting in varying assessment methods and metrics from different measurement system providers. Interpreting metric values also proves challenging for most software users due to a lack of clarity and informativeness. Furthermore, it has been discovered that while decisions based on cost, development duration, and designer company reputation influence software implementation choices, they do not always guarantee optimal software quality.

Information technology for prediction of software quality level

Today, there is a contradiction between the rapid increase in the complexity and size of modern software while increasing responsibility for the performance of their functions, the increasing requirements of customers and users to the quality and efficiency of software use and the imperfection of models, methods, tools of predicting software quality at the early stages of the life cycle.Therefore, the task of predicting the software quality level based on requirements is relevant. The aim of this study is to solve this task by developing information technology for prediction software quality levels based on requirements. The proposed information technology for prediction software quality level based on requirements provides analysis of quality attributes in requirements, reflects the dependence (equations) of quality characteristics on attributes, forms a quantitative assessment of quality characteristics, reflects the dependence (equation) of quality on its characteristics, forms a quantitative assessment of quality, performs quality level prediction, provides all the listed services simultaneously and the model, methods, and tools underlying the IT belong to common methodological approaches and are integrated. The developed system of predicting the software quality level based on requirements provides the user with predicted estimates of eight software quality characteristics, geometric interpretation of the software quality characteristics’ values, a comprehensive indicator of the predicted software quality, and a conclusion about the future software quality level. On the basis of this, it is possible to compare sets of requirements for software and make a reasonable choice of a set of requirements for further implementation. The information technology and the system of predicting the software quality level based on requirements, developed in this paper, provide the possibility of comparing sets of requirements for software, justified selection of requirements for further implementation of quality software (as experiments have shown, this is only one of the four proposed sets), and rejection or revision of unsuccessful sets of requirements that cannot be used to develop quality software.

Using social media and personality traits to assess software developers' emotional polarity.

Although human factors (e.g., cognitive functions, behaviors and skills, human error models, etc.) are key elements to improve software development productivity and quality, the role of software developers' emotions and their personality traits in software engineering still needs to be studied. A major difficulty is in assessing developers' emotions, leading to the classic problem of having difficulties understanding what cannot be easily measured. Existing approaches to infer emotions, such as facial expressions, self-assessed surveys, and biometric sensors, imply considerable intrusiveness on developers and tend to be used only during normal working periods. This article proposes to assess the feasibility of using social media posts (e.g., developers' posts on Twitter) to accurately determine the polarity of emotions of software developers over extended periods in a non-intrusive manner, allowing the identification of potentially abnormal periods of negative or positive sentiments of developers that may affect software development productivity or software quality. Our results suggested that Twitter data can serve as a valid source for accurately inferring the polarity of emotions. We evaluated 31 combinations of unsupervised lexicon-based techniques using a dataset with 79,029 public posts from Twitter from sixteen software developers, achieving a macro F1-Score of 0.745 and 76.8% of accuracy with the ensemble comprised of SentiStrength, Sentilex-PT, and LIWC2015_PT lexicons. Among other results, we found a statistically significant difference in tweets' polarities posted during working and non-working periods for 31.25% of the participants, suggesting that emotional polarity monitoring outside working hours could also be relevant. We also assessed the Big Five personality traits of the developers and preliminarily used them to ponder the polarities inferences. In this context, Openness, Conscientiousness, and Extraversion were frequently related to neutral and positive posts, while Neuroticism is associated with negative posts. Our results show that the proposed approach is accurate enough to constitute a simple and non-intrusive alternative to existing methods. Tools using this approach can be applied in real software development environments to support software team workers in making decisions to improve the software development process.

A mixed method study of DevOps challenges

DevOps practices combine software development and IT (Information Technology) operations. The continuous needs for rapid but quality software development requires the adoption of high-quality DevOps tools. There is a growing number of DevOps related posts in popular online developer forum Stack Overflow (SO). While previous research analyzed SO posts related to build/release engineering, we are aware of no research that specifically focused on DevOps related discussions. This paper aims to learn the challenges developers face while using the currently available DevOps tools and techniques along with the organizational challenges in DevOps practices. We conduct an empirical study by applying topic modeling on 174K SO posts that contain DevOps discussions. We then validate and extend the empirical study findings with a survey of 21 professional DevOps practitioners. We find that: (1) There are 23 DevOps topics grouped into four categories: Cloud & CI/CD Tools, Infrastructure as Code, Container & Orchestration, and Quality Assurance. (2) The topic category ‘Cloud & CI/CD Tools’ contains the highest number of topics (10) which cover 48.6% of all questions in our dataset, followed by the category Infrastructure as Code (28.9%). (3) The file management is the most popular topic followed by Jenkins Pipeline, while infrastructural Exception Handling and Jenkins Distributed Architecture are the most difficult topics (with least accepted answers). (4) In the survey, developers mention that it requires hands-on experience before current DevOps tools can be considered easy. They raised the needs for better documentation and learning resources to learn the rapidly changing DevOps tools and techniques. Practitioners also emphasized on the formal training approach by the organizations for DevOps skill development. Architects and managers can use the findings of this research to adopt appropriate DevOps technologies, and organizations can design tool or process specific DevOps training programs.

The Metric for Automatic Code Generation Based on Dynamic Abstract Syntax Tree

In order to improve the efficiency and quality of software development, automatic code generation technology is the current focus. The quality of the code generated by the automatic code generation technology is also an important issue. However, existing metrics for code automatic generation ignore that the programming process is a continuous dynamic changeable process. So the metric is a dynamic process. This article proposes a metric method based on dynamic abstract syntax tree (DAST). More specifically, the method first builds a DAST through the interaction in behavior information between the automatic code generation tool and programmer. Then the measurement contents are extracted on the DAST. Finally, the metric is completed with contents extracted. The experiment results show that the method can effectively realize the metrics of automatic code generation. Compared with the MAST method, the method in this article can improve the convergence speed by 80% when training the model, and can shorten the time-consuming by an average of 46% when doing the metric prediction.

A Hybrid Software Defects Prediction Model for Imbalance Datasets Using Machine Learning Techniques: (S-SVM Model)

Software defect prediction (SDP) is an essential task for developing quality software, and various models have been developed for this purpose. However, the imbalanced nature of software defect datasets has challenged these models, resulting in decreased performance. To address this challenge, the author has proposed a hybrid machine learning model that combines Synthetic Minority Oversampling Technique (SMOTE) with Support Vector Machine (SVM)—SMOTE-SVM (S-SVM) model. The author has empirically examined SDP using multiple datasets (CM1, PC1, JM1, PC3, KC1, EQ and JDT) from the PROMISE and AEEEM repositories. The experimental study indicates that the S-SVM model involved training and compared with previously developed balanced and imbalanced test datasets using four evaluation metrics: Precision, Recall, F1 score, and Accuracy. For the balanced dataset, the S-SVM model achieved precision values ranging from 70 to 96, recall values ranging from 52 to 94, F1-score values ranging from 67 to 90, and accuracy values ranging from 69 to 98. For the imbalanced dataset, the S-SVM model achieved precision values ranging from 60 to 93, recall values ranging from 64 to 97, F1-score values ranging from 69 to 91, and accuracy values ranging from 67 to 87. The proposed S-SVM model outperforms other models’ ability to classify and predict software defects. Therefore, the hybridisation of SMOTE and SVM improved the model’s ability to categories and predict balanced and imbalanced datasets when sufficient defective and non-defective data is provided.