An Online Developer Profiling Tool Based on Analysis of GitLab Repositories

Early prediction of quality of service using interface-level metrics, code-level metrics, and antipatterns

There exists a large body of work on the specification and detection of anti-patterns in the source code of software systems. However, there are very few studies on the origins of the occurrences of anti-patterns in the source code: do the very design of the systems lead to the occurrences of anti-patterns or are anti-patterns introduced during implementation? Knowing when anti-patterns are introduced could help software designers and developers improve the quality of the source code, for example by eliminating fault-prone anti-patterns early during the design of the systems, even before their implementation. Therefore, we detect occurrences of anti-patterns in design models and in the source code of some systems, trace these occurrences between design and implementation, and study their relation and impact on the source code. First, we analyze both the UML design models and the source code of 10 open-source systems and show that antipatterns exist in design models. We observe that, on average, 37% of the classes in the design models that belong to anti-patterns also exist in the source code and also play roles in the same anti-patterns. Second, we investigate two open-source systems to assess the impact of the anti-patterns in their design models on the source code in terms of changes and faults. We show that classes that have anti-patterns in the design models have more changes and faults in the source code. Our results suggest that the design of the systems lead to anti-patterns and that the antipatterns impact negatively the change-and fault-proneness of the classes in the source code. Thus, designers should be wary of anti-patterns in their design models and could benefit from tools that detect and trace these anti-patterns into the source code.

This research investigates the impact of prompt engineering on the efficiency, code quality, and security of CRUD (Create, Read, Update, Delete) operations in software development using large language models (LLMs) like ChatGPT. Prompt engineering, which involves crafting specific inputs to guide AI outputs, has become crucial for code generation, debugging, and vulnerability detection tasks. The study addresses three key research questions: identifying the most influential aspects of prompt engineering on CRUD efficiency compared to traditional coding, recognising common error patterns in AI-generated code and preemptive mitigation strategies, and leveraging human-AI collaboration to optimise CRUD application development. This study evaluates 52 relevant papers from 2018 to 2023 through a systematic literature review from Google Scholar, ResearchGate, arXiv, and Sci-Hub. The findings indicate that effective, prompt engineering significantly enhances productivity, reduces development time, and improves code adaptability to complex data structures. Techniques such as role-prompting and chain-of-thought prompting are highlighted for their ability to produce high-quality code, reducing errors and enhancing overall code quality. The review revealed significant results, including identifying prompt engineering techniques that substantially improve the performance of LLMs in CRUD operations. ChatGPT, in particular, excels in generating and debugging code but struggles with more complex tasks and security vulnerabilities. The study emphasises the need for continuous human oversight to ensure security and correctness, addressing challenges such as managing programming variability and the stochastic nature of LLMs.The research concludes that integrating ChatGPT into CRUD operations significantly advances software development, improving efficiency, code quality, and security. However, it is crucial to address its limitations through better prompt engineering and continuous human involvement. Future work will involve developing a fully functional CRUD application using generative AI, further evaluating its practical applications and limitations in software development.

Software Quality Assurance is a sub-process that ensures that developed software meets and complies with defined or standardized quality specifications. Focusing on source code, there are characteristics that can be used to evaluate the quality. Introductory courses must encourage freshmen students to improve internal quality of their source code, but only as sophomore they have contact with Software Engineering concepts, including Quality Assurance. In this paper we present a tool to source code quality evaluation aimed at supporting students to improve their source code and, consequently, their programming skills. The proposed tool uses quality reports (available to professional environment integrate with software repositories) to analyze students' source code and provide a feedback about the student coding. The proposed tool run locally, with few computational resources. In addition, we proposed the methodology to use the proposed tool: it consists of challenging students to perform a set of maintenance tasks in a controlled environment. We prepared a source code by introducing common defects, what decreases the quality of source code, and ask to students to perform maintenance tasks in order to both eliminate the introduced defects and introduce new features. After each modification, the students must evaluate their code using the proposed tool to obtain a feedback about quality of source code. To evaluate the approach and the tool, we created a survey and applied to students and the teacher. As a result, we show the benefits of using the proposed tool to both teachers and students perspectives. The results are positive to enhance the teaching-learning Software Quality Assurance to Software Engineering students.

Reading and understanding source code is a major task in software development. Code comprehension depends on the quality of code, which is impacted by code structure and identifier naming. In this paper we empirically investigated whether longer but more descriptive identifier names improve code comprehension compared to short names, as they represent useful information in more detail. In a web-based study 88 Java developers were asked to locate a semantic defect in source code snippets. With descriptive identifier names, developers spent more time in the lines of code before the actual defect occurred and changed their reading direction less often, finding the semantic defect about 14% faster than with shorter but less descriptive identifier names. These effects disappeared when developers searched for a syntax error, i.e., when no in-depth understanding of the code was required. Interestingly, the style of identifier names had a clear impact on program comprehension for more experienced developers but not for less experienced developers.

A key step in Model-Based Design is the deployment of an algorithm as machine code onto a target processor in the production vehicle. Modern software tools automatically generate the algorithmic source code from models. Given the many combinatorial possibilities for realizing a given algorithm within the modeling environment, the generated C source code will be a function of a realization. This dependency is an important consideration because the quality and clarity of the source code impacts the amount of verification and analysis that must be done for production software development. Other factors involved in generating the machine code from the source code, such as compiler optimization and microprocessor architecture, also contribute to this optimization. Organizations that proactively data mine and gather these optimizations into a set of best practices stand to benefit from reduced development times and lower costs. This paper introduces techniques that can be used to generate and measure code constructs used to create a set of best practices for the Simulink modeling environment. The quality of the object code is measured by examining the algorithm compiled within an Integrated Development Environment.

Software development productivity, code quality, and knowledge sharing have all increased dramatically with the use of AI code assistants like GitHub Copilot. These technologies provide real-time suggestions, expedite workflows, and speed up prototyping by utilising massive language models that have been trained on extensive code repositories. However, serious questions about software security, developer dependency, and ethical ramifications are brought up by their increasing use. The advantages and disadvantages of AI code helpers for safe software development are discussed in this study. It highlights how crucial human oversight, frequent security audits, ongoing model upgrades, and conformity to DevSecOps and OWASP guidelines are. In the end, software integrity cannot be jeopardised by including AI into development processes. In an increasingly automated development context, strategic implementation is crucial to maximising AI's benefits while protecting against new risks.

Achieving comprehensive code quality and ensuring software reliability are critical goals in modern software engineering. This paper delves into advanced strategies that encompass both technical and organizational practices, aiming to enhance code quality and boost software reliability. Key strategies include the implementation of rigorous code review processes, adoption of automated testing frameworks, and the utilization of static and dynamic code analysis tools. Firstly, rigorous code review processes are fundamental for maintaining high standards of code quality. By fostering a collaborative environment where peer reviews are routine, teams can catch potential issues early, ensure adherence to coding standards, and promote knowledge sharing. This practice not only improves code quality but also enhances team cohesion and expertise. Secondly, automated testing frameworks are indispensable in achieving comprehensive test coverage and reducing the incidence of bugs. Unit tests, integration tests, and end-to-end tests, when automated, provide continuous feedback and ensure that code changes do not introduce new defects. This continuous testing approach is crucial for maintaining software reliability, especially in agile and DevOps environments where rapid iterations are common. Furthermore, the integration of static and dynamic code analysis tools into the development pipeline offers additional layers of quality assurance. Static analysis tools inspect the codebase for potential vulnerabilities, code smells, and compliance with coding standards without executing the program. Dynamic analysis tools, on the other hand, evaluate the software during runtime to detect memory leaks, concurrency issues, and performance bottlenecks. Together, these tools provide a comprehensive assessment of the code’s health and reliability. Moreover, fostering a culture of continuous improvement and learning within the development team is essential. Regular training sessions, workshops, and knowledge-sharing activities help in keeping the team updated with the latest advancements in software engineering practices and tools. Encouraging a mindset of quality-first and reliability among developers ensures long-term benefits and sustainability. In conclusion, achieving comprehensive code quality and ensuring software reliability require a multifaceted approach that integrates rigorous code reviews, automated testing frameworks, and both static and dynamic analysis tools. Coupled with a strong culture of continuous learning and improvement, these strategies collectively contribute to the development of robust, reliable, and high-quality software systems. Keywords: Software Reliability, Code Quality, Advanced, Strategies, Comprehensive.

The rapid evolution of Artificial Intelligence (AI) has brought about significant advancements in multiple domains, including software development. One of the most promising innovations is AI-powered code generation through Large Language Models (LLMs), such as OpenAI’s GPT-3 and GPT-4. These models, having been trained on large amounts of programming data, have the ability to produce human-readable code from natural language inputs, which is a big potential for simplifying and optimizing software development processes. The aim of this paper is to analyze the performance of LLMs in automated software development by testing their performance on a variety of tasks such as code generation, debugging, and optimization of software. The research explores both the strengths and weaknesses that these models have to offer, in terms of some of the most important indicators like code quality, generation time, and maintainability of the code. According to our observation, although LLMs hold immense potential to automate mundane programming tasks and enhance developer productivity, they still struggle to cope with more intricate, domain-specific programming tasks involving a higher level of understanding, for example, designing architectures and top-level decision-making. In spite of such shortcomings, LLMs can tremendously enhance software development processes, particularly for small-scale projects or act as helpers for more senior developers. The paper summarizes by reflecting on the potential for LLMs to transform software development processes in the future, while also the importance of the model's reliability, coding quality, and security to be improved if it is to be made applicable to larger, more crucial uses.

Though abundant source code repositories are available on code repository hosting platforms, their detailed code quality information is not available readily. Software engineering researchers often need to select a set of high-quality repositories. Despite the code quality is an important concern for repository selection, the lack of this information makes researchers depend on alternatives such as the number of issues and the number of stars associated with repositories. Furthermore, practitioners expect user-friendly visual ways to assess the quality of their projects during the evolution of the codebase without putting a considerable effort. We propose an open platform QScored to fill the gap for both researchers and practitioners. The platform hosts detailed code quality analysis information for a large number of repositories (currently more than twelve thousand containing more than 200 million LOC), computes quality score and assigns relative ranking of the hosted repositories based on detected architecture, design, and implementation smells, as well as offers a comprehensive set of visualization aids for code quality aspects. Furthermore, the platform provides REST APIs to search repositories based on their code quality scores and ranking of hosted software projects.Video of the demo: https://youtu.be/-IgvjGV-2X0

In February 2020, the CPC Central Committee defined the epidemic at a meeting which with the theme of "Coordinating the epidemic prevention and control with economic and social development”—“The COVID-19 outbreak is a major public health emergency that has spread in the fastest speed, caused the most extensive infection and is the most difficult to contain in the country since the founding of the People's Republic of China”. From this definition, it can be seen that the impact of this epidemic on China is very huge. Sudden outbreak of the COVID-19 inevitably deal a relatively big blow to China's economic and social development. How to adopt effective measures to promote economic and social recovery and development under the working on an ongoing basis to contain the virus is an urgent issue that needs to be resolved. This article takes the Coastal Economic Zone of Guangdong Province as a research object. Analyzes the impact of the epidemic on it, and the difficulties and problems facing the current economic development. In response to these problems, this article makes several suggestions: continued prevention and control of the epidemic situation, increased government support, optimized industrial structure, and promoted economic growth by the Troika. So as to promote Guangdong's economic recovery and further promote Guangdong's economic and social development.

Source code quality is undoubtedly very important, yet usually forgotten aspect of software development process. This study shows different approaches to analysis of student codes, mainly in Java. By comparing different widely-used tools and our solution, we are able to get precise results from such an analysis, and thus to improve the code quality. The analysis results can also be used to determine knowledge and skills of novice programmers (students). Analysis of source codes may be as simple as counting the occurrences of language constructs or as complex as design pattern detection. Different approaches and solutions, i.e. measurements of language construct occurrences, copy-pasted code and design pattern detection, or source code quality, are discussed and subjected to evaluation by being executing on source codes of students as novice programmers.

The purpose of this study is to explore the social outcomes of older adult learning in Taiwan. In light of our society's aging population structure, the task of establishing evaluation framework and indicators for the social outcomes of learning (SOL) as applied to older adults is urgent. In order to construct evaluation indicators for older adult SOL based on the framework provided by the Organization for Economic Co-operation and Development (OECD; 2007), the focus group method and Delphi Technique were adopted. Five findings stand out: First, the evaluation framework of older adult SOL includes two domains, seven dimensions, 21 subdimensions, and 58 evaluation indicators. Second, the evaluation indicators were ranged between important and very important. Third, there are 26 evaluation indicators identified as primary indicators. Fourth, the evaluation framework of older adult SOL extends from individual to the external worlds. Fifth, the community and social dimensions of older adult SOL need to be reinforced.

A software system undergoes many code changes, which may affect code quality and maintainability. Refactoring techniques are a common strategy for restructuring source code and improving internal architecture. However, refactoring activities are limited to a catalog of well-known modifications, and we intend to investigate modifications to the source code, looking for patterns that affect its internal quality. In this work, we present a process for identifying code improvements and then learning from the modifications. This process extracts syntactic patterns of code changes and classifies them based on their impact on software maintainability. We executed the classification process on 132,819 code changes from seven GitHub repositories, obtaining 2405 significant code change patterns. Most code change patterns involve simple modifications to structural elements that have only a slight impact on maintainability. These modifications include renaming or moving code elements, simple refactoring, and adding a new implementation. We analyzed both simple modification patterns and many modifications to identify their impact on internal source code quality. The results show how significant a subset of identified patterns is to internal source code quality, as it improves or reduces maintainability variation.

The power industry is the basic industry of the national economy, which can promote the economic development and enhance the living standard. Meanwhile, as the key energy consumption and energy-saving field, the power industry plays an important role in supporting the national economic and social sustainable development. In recent years, the power industry experienced rapid development in China. The report introduces the active efforts and contributions made by the Chinese power industry to the national economic and social sustainable development from multiple aspects and angles with data and examples: 1) With the optimization of power generation and grid structure, promotion of advanced energy-saving technology and enhancement of management level, the technical indicators such as net coal consumption rate, auxiliary power ratio, line loss rate, etc. have been continuously improved. For example, net coal consumption rate of supply reduces from 370tce/kWh in 2005 to 330tce/kWh in 2010, while line loss rate reduces from 7.21% to 6.53%, with a decrease of about 10% during a Five-year Period. The accumulatively 300 million tce of energy conservation has realized outstanding performance; 2) The cleanly energy generators have realized great development with increasing proportion from 23.3% in 2005 to 26.2% in 2010, in which the renewable energy generators gave the main contribution. There has been accumulatively 300 million tce of fossil energy conservation during the period; 3) Desulfurization equipment has developed with very high coverage rate. Fuel gas desulfurization unit was little in 2005, and accounts for 89% of the total coal installed capacity in 2010. The effect of emission reduction is obviously that the emission of sulfur dioxide per unit of thermal power output has decreased by over 60% compared with that in 2005; 4) The national cross-regional grid has come into being, the cross-regional electric energy transmission has been greatly increased and the power grid has played a more and more important role in the resource optimization and allocation, which satisfies the demand of the imbalance between the resource location and power demand focus; 5) The Energy Service Companies (ESCo) subject to power enterprises have been gradually established, that promotes the energy-saving and power Demand-side Management (DSM), brings notable effects from energy management and load management. With the higher influence of the developing concepts such as the low carbon and environmental-friendly in the social development, Chinese economy would realize the sustainable and great-leap-forward new industrialization in future. The power industry will make active contributions through the following measures: To keep on strengthening the power structure adjustment and increasing the proportion of the renewable energy in the power generation; To focus on the upgrade of energy-saving power generation technology, and the development of large-capacity, high-efficiency and high-parameter generators; To emphasize the upgrade of the power grid energy-saving technology and the development of the Ultra High-voltage (UHV) transmission technology, expand the trans-regional network establishment, and strengthen the power generation dispatching and the transaction of generation right; To carry out the renovation of the environmental protection technology, increase the coverage rate of the desulfurization equipment and denitration equipment, and research and apply the Carbon Capture and Storage (CCS) technology; To enhance the management level of energy conservation and emission reduction, and promote the power DSM. In 2020, the proportion of the installation capacity of the renewable-energy-source generators will increase very quickly, the proportion of the single capacity of 600 MW and above in the thermal power generation unit will be over 50%, the coverage rate of the desulfurization equipment will be over 95%, and the coverage rate of the denitration equipment will be about 80%. Calculated according to the Integrated Resource Strategy Planning (IRSP) model, the potential capacity of Efficiency Power Plant (EPP) would be 200 GW, accounting for about 10% of the conventional installed capacity. In the following decade, some indicators of power industry will be the international advanced level, which will promote the national economic and social sustainable development in the aspect of the technology and management, etc.