Abdessalam Elhabbash
Abstract
Background: Self-awareness has been recently receiving attention in computing systems for enriching autonomous software systems operating in dynamic environments.
Objective: We aim to investigate the adoption of computational self-awareness concepts in autonomic software systems and motivate future research directions on self-awareness and related problems.
Method: We conducted a systemic literature review to compile the studies related to the adoption of self-awareness in software engineering and explore how self-awareness is engineered and incorporated in software systems. From 865 studies, 74 studies have been selected as primary studies. We have analysed the studies from multiple perspectives, such as motivation, inspiration, and engineering approaches, among others.
Results: Results have shown that self-awareness has been used to enable self-adaptation in systems that exhibit uncertain and dynamic behaviour. Though there have been recent attempts to define and engineer self-awareness in software engineering, there is no consensus on the definition of self-awareness. Also, the distinction between self-aware and self-adaptive systems has not been systematically treated.
Conclusions: Our survey reveals that self-awareness for software systems is still a formative field and that there is growing attention to incorporate self-awareness for better reasoning about the adaptation decision in autonomic systems. Many pending issues and open problems outline possible research directions.
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Index Terms
- Self-awareness in Software Engineering: A Systematic Literature Review
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Reviews
Andrea F Paramithiotti
As researchers in the field of distributed software systems, the authors feel as though software self-awareness is becoming more and more fundamental to their work. On the one hand, they see a great number of scientific publications available; on the other, they feel these publications are not yet well framed within computer science as a whole. So, in response, they undertook an extensive survey of about 70 scientific papers to fill this gap. This literature review is systematic and thorough. The authors start from several broad, qualitative criteria: (i) how the studies have defined and characterised self-awareness, (ii) what inspired researchers when engineering self-awareness, (iii) what motivates researchers to use self-awareness, (iv) what software engineering practices and software paradigms have employed self-awareness, (v) how computational self-awareness is engineered to encode self-awareness properties within software systems, (vi) how the proposed self-aware systems are evaluated to quantify the accompanying benefits and overheads, and (vii) the real-world applications that are using self-awareness. From these criteria, they created a full-fledged review protocol composed of seven questions: RQ1. How to define and characterise self-awareness RQ2. What motivated the application of self-awareness in software engineering RQ3. What are the sources of inspiration for its engineering RQ4. In which software engineering practices and software paradigms is self-awareness employed RQ5. What are the approaches for engineering self-awareness RQ6. How are self-aware software systems evaluated RQ7. What are the working real-world applications adopting computational self- awareness Each paper is analyzed and in-depth, reporting where it stands in regard to each question. Tables and graphs present the results. By the end of their review, the authors have created a complete taxonomy of software self-awareness. The authors highlight a few shortcomings of the studies as a whole: a lack of proactive adaptation in the systems and algorithms studied; humans are still massively in the loop in supervision roles; unclear goal mapping by these software paradigms; and concerns about human privacy. On the other hand, they find the implications of these studies on future research deep, challenging, and rewarding. The authors also honestly point out some limitations of their own work: some relevant studies are missing, possible selection bias toward the most-cited papers, and possible inaccuracy in data extraction. The appendices report not only references to all studies cited and other relevant papers, but also the complete set of criteria, from review planning to data gathering to result presentation, which are the mark of a serious and comprehensive study that will undoubtedly benefit researchers in their future work.
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