Journal of Architectural Engineering Technology
Open Access

Building; Architecture; Bio-adaptive model

Introduction

Our daily lives are becoming increasingly reliant on various software applications and autonomous systems, ranging from transportation1 to healthcare2. However, in recent years, many people have been severely impacted by software bugs (for example, hundreds of people died in a plane crash caused by a software glitch3), as well as security vulnerabilities in software services (such as the ransomware attack on energy sector4). Furthermore, regulatory inspections of software internal functions by government authorities are increasing due to various concerns such as hidden unethical business practises, data privacy, and human values (racial bias). These are some of the outside challenges that a software project may face. To keep up with rapidly evolving technologies and requirements, the software industry may require additional time and manpower (cost) for regular development activities, late-life-cycle change, and continuous integration glitches5. [1, 2].

Design evaluation

Many studies have focused on software architectural change detection and classification (ACDC) in order to develop automated tools for the aforementioned applications. Various properties are extracted from the textual description and source code of the change tasks during detection and classification (such as commits). Researchers are investigating those properties using machine learning, natural language processing, and non-traditional techniques to create supportive tools.

To study architectural change, ground truth architecture at a given point in time (or a version) is extracted, and architectural element modifications are detected. Several studies propose change metrics for detecting architectural change instances to that end [3].

Our evidence analysis reveals a lack of lightweight techniques capable of processing hundreds of thousands of change revisions of a code base contained in a single release without human intervention or longer delays. We also anticipate that no single approach will be the best fit for detecting change instances because the deployment of an approach for a specific scenario is dependent on the types of architecture (and its views) and abstraction levels that the development team focuses on. ARCADE is the most promising architectural change analysis tool that employs several popular change detection metrics [4,5].

MoJo, MoJoFM, and A2A are all examples of MoJo. We also recognise that in order to effectively deploy detection techniques in the design review process, efficient techniques for capturing the design decision associativity8 of the changed elements are required. However, one of the major issues in architectural change classification that remains unresolved is when a committed change task contains tangled changes (when multiple unrelated issues, such as new feature addition and bug fixing, are implemented in a single commit). Wang and colleagues (2019b). [6, 7].

Conclusion

We present our investigation of existing architectural (both prescriptive and descriptive) change detection and classification techniques in this paper. Throughout the investigation, we compare the claims made by the authors of those studies based on a variety of factors that must be considered when working with architecture-centric software development. We discovered that the majority of existing techniques require either manual intervention or input generation tools that are often inefficient to process. [8, 9, 10].

Acknowledgement

We are grateful to the anonymous reviewers for their insightful comments. The Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grants, an NSERC Collaborative Research and Training Experience (CREATE) grant, and two Canada First Research Excellence Fund (CFREF) grants coordinated by the Global Institute for Food Security (GIFS) and the Global Institute for Water Security (GIWS) have all contributed to this research.

Declaration of interest statement

The authors declare no conflict of interest.

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