مشخصات مقاله:
عنوان فارسی مقاله:
مهندسی نرم افزار هوشمند در زمینه توسعه نرم افزار چابک: مرور سیستماتیک نوشته ها
عنوان انگلیسی مقاله:
Intelligent software engineering in the context of agile software development: A systematic literature review
کلمات کلیدی مقاله:
مهندسی نرم افزار هوشمند، توسعه نرم افزار چابک، مهندسی نرم افزار مبتنی بر جستجو، یادگیری ماشین، شبکه های بیزی، هوش مصنوعی
کلمات کلیدی انگلیسی:
Intelligent Software Engineering – Agile Software Development – Search-based Software Engineering – Machine Learning – Bayesian networks – Artificial intelligence
مناسب برای رشته های دانشگاهی زیر:
مهندسی کامپیوتر
مناسب برای گرایش های دانشگاهی زیر:
مهندسی نرم افزار
وضعیت مقاله انگلیسی و ترجمه:
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فهرست مطالب:
Abstract
Keywords
1. Introduction
2. Research methodology
2.1. Research questions
2.2. Search method
2.2.1. Search terms
2.2.2. Data sources
2.3. Selection criteria
2.3.1. Selection method
2.4. Quality assessment
2.5. Data extraction and classification scheme
3. Results and analysis
3.1. Overview of the systematic review process
3.2. Quality assessment
3.3. Types of research contributions
3.4. Intelligent techniques applied to ASD
3.5. Applications of intelligent techniques on ASD
3.5.1. Software engineering management
3.5.2. Software requirements
3.5.3. Software engineering process
3.5.4. Software design
3.6. Publication frequency
3.7. Maturity of current intelligent techniques applied to ASD
3.8. Risks on applying intelligent techniques to ASD
4. Implications for research and practice
5. Threats to validity
6. Conclusion and future work
Complete list of selected papers
References
قسمتی از مقاله انگلیسی:
1. Introduction
Since its inception, the primary goal of software engineering is to improve software quality and productivity. For this purpose, many initiatives have emerged, including maturity models, formal methods [1], reuse-driven software 5 engineering [2], and value-based software engineering [3]. More recently, with the increasing amount of data generated by tools such as software versioning systems (e.g., Git1 ), build management systems (e.g., Jenkins2 ) and project management platforms (e.g., Jira3 ), an emergent field is appearing: the Intelligent Software Engineering (ISE). Xie [4, 5] defines ISE 10 with two perspectives. First, as the application of artificial intelligence (AI) technologies to software engineering. Second, as the development of software engineering solutions for intelligent software. In this study, we only focus on Xie’s first perspective. We complement it by defining the term “intelligent technique” as the exploration of data (from digital artifacts or domain experts) for 15 knowledge discovery, reasoning, learning, planning, natural language processing, perception or supporting decision-making. Therefore, in the context of this work, data mining, fuzzy logic, machine learning, expert systems, and search algorithms (e.g., swarm intelligence, evolutionary algorithms) are examples of intelligent techniques. On the other hand, we do not consider formal methods, 20 software process simulation modeling [6], and model-driven development [7] as intelligent techniques. In this context, examples of themes included under ISE are the application of search and optimization [8], machine learning [9], recommender systems [10], Bayesian networks [11], software analytics [12], big data analysis [13] and decision analysis in software engineering [14, 15].