Description:FIELD OF THE INVENTION

The embodiments of the present invention generally relates to the field of Software Architecture Recovery. More particularly, the present invention relates to applying Software Features and Meta-Heuristics for Quality-Centric Recovery of Object-Oriented Software Architecture.
BACKGROUND OF THE INVENTION

The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

Software architecture recovery (SAR) is a critical process in understanding and maintaining complex software systems. Traditional approaches often lack efficiency and accuracy in representing dependencies among software elements, hindering effective recovery of software architectures.

Modern software systems are intricate and multifaceted, composed of numerous interconnected components that collaborate to deliver complex functionalities. As software systems evolve over time to accommodate changing requirements and technologies, their architecture can become convoluted, making it challenging to comprehend, maintain, and extend these systems efficiently. The need for effective software architecture recovery (SAR) methods has become paramount to address these challenges and facilitate seamless software maintenance, evolution, and decision-making processes.

Traditional software architecture recovery approaches often fall short in accurately representing the intricate dependencies, relationships, and behavior of software elements within a system. Many existing methods rely solely on static analysis of code, disregarding crucial aspects such as semantic context, dynamic interactions, and evolutionary history. Consequently, these methods may lead to suboptimal architectural models that hinder effective understanding and maintenance of the software.

The proposed invention seeks to overcome these limitations by introducing an innovative SAR approach that harnesses a synergistic combination of diverse features and techniques. By incorporating structural, semantic, evolutionary, dynamic, and directory-based attributes, the invention aims to provide a holistic and accurate representation of the software architecture. This comprehensive view enables software architects, developers, and maintenance teams to gain deeper insights into the architecture's intricacies and facilitates effective decision-making.

The incorporation of soft computing techniques for class-level clustering enhances the accuracy of architectural recovery. Unlike traditional clustering methods, which may produce rigid and oversimplified groupings, the soft computing approach enables the identification of subtle nuances and intricate relationships among software components. This results in more meaningful and context-aware architectural partitions that align with the system's inherent complexity.

Furthermore, the invention's emphasis on quality parameter evaluation introduces a systematic and objective mechanism for assessing the recovered architectural solutions. By quantifying cluster coherence, stability over time, and adaptability to changes, the invention ensures that the selected architectural representation aligns with the software's performance, robustness, and responsiveness.

The culmination of these innovative aspects positions the proposed SAR approach as a significant advancement in the field of software engineering. It addresses the growing demand for accurate and comprehensive architectural recovery methods that can cope with the challenges posed by modern software systems. By promoting a deeper understanding of software architectures and enabling more informed decisions, the invention has the potential to revolutionize the way software systems are maintained, evolved, and managed.

The present invention arises from the need to address the limitations of existing software architecture recovery methods and provide a novel approach that leverages multiple features, soft computing techniques, and quality parameter evaluation. This background sets the stage for the invention's contribution to the field of software engineering, offering a powerful tool for

unraveling the complexities of software architectures and enhancing the management of software systems in a dynamic and ever-evolving technological landscape.

There is therefore a need in the art to apply a method that can facilitate improved software features and meta-heuristics for quality-centric recovery of object-oriented software architecture.

OBJECTIVE OF THE INVENTION

Some of the objects of the present disclosure, which at least one embodiment herein satisfies are listed herein below.

The primary objective of the present invention is to apply software features and meta-heuristics for quality-centric recovery of object-oriented software architecture.

SUMMARY OF THE INVENTION

This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.

In an aspect, the present invention generally relates to the field of Software Architecture Recovery. More particularly, the present invention relates to applying Software Features and Meta-Heuristics for Quality-Centric Recovery of Object-Oriented Software Architecture.
The present invention proposes a novel approach for software architecture recovery that leverages multiple features, including structural, semantic, evolutionary, dynamic, and directory- based attributes of software elements. The approach involves clustering at the class level using an efficient soft computing method and evaluating recovered clusters based on various quality parameters.
BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated herein, and constitute a part of this invention, illustrate exemplary embodiments of the disclosed methods and systems in which like

reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present invention. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that invention of such drawings includes the invention of electrical components, electronic components or circuitry commonly used to implement such components.

FIG. 1 illustrates an exemplary framework in which or with which the present invention apply software features and meta-heuristics for quality-centric recovery of object-oriented software architecture, in accordance with an embodiment of the present disclosure.

DETAIL DESCRIPTION OF THE INVENTION

In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the

embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.

Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.

Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same

embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.

Software architecture has emerged as an important area of software engineering research and practice over the last two decades. The increasing size of, complexity of, and demand for quality in software systems are some of the most important factors that have resulted in sustained interests in software architecture research and practice. The architecture of a software system can be thought of as its blueprint. Overtime, software suffers from architectural erosion and it requires recovery in order to reduce involved future maintenance activities along with long sustainability of the considered project. Architecting or SAR is a process of conceiving, defining, expressing, documenting, communicating, certifying proper implementation of, maintaining and improving an architecture throughout a system’s life cycle.

The key steps of the proposed software architecture recovery (SAR) approach are as follows:

1. Utilization of Features: In one embodiment of the invention, structural, semantic, evolutionary, dynamic, and directory-based features of software elements are employed to construct a holistic representation of the software architecture. These features provide a multifaceted view of the relationships and interactions between different components within the software system.

• Structural Features: The structural features encompass class relationships, inheritance hierarchies, method invocations, and other static dependencies among software elements. These features capture the foundational relationships that define the architecture.

• Semantic Features: Semantic similarity between software elements is employed to identify components that exhibit similar behavior or functionality. By analyzing the semantic context of these elements, the invention enhances the accuracy of architectural recovery.

• Evolutionary Features: Version history and change patterns of software elements over time provide insight into the evolution of the software architecture. This helps in understanding how the architecture has evolved and adapted to changing requirements.

• Dynamic Features: Runtime behavior and interactions among software elements during execution reveal runtime dependencies and interactions, aiding in the reconstruction of the architecture.

• Directory-Based Features: The organization and placement of software elements within a directory structure offer clues about logical and functional groupings. Leveraging directory-based features contributes to a more coherent architectural representation.

2. Clustering at Class Level: The proposed approach involves performing clustering at the class level. Clustering is a technique that groups similar software elements together based on their attributes and relationships. In this invention, an efficient soft computing method is employed to perform clustering, ensuring accurate and meaningful groupings. The soft computing method may include fuzzy clustering, neural network-based clustering, or genetic algorithm-based clustering.

The class-level clustering is crucial for identifying modules or components within the software architecture that exhibit cohesive behavior. It aids in partitioning the software system into logical units, which can subsequently be used to construct a higher-level architectural view.

3. Quality Parameter Evaluation: Once the clustering is performed, the recovered clusters are evaluated using various quality parameters. These quality parameters are designed to assess the coherence, stability, and effectiveness of the recovered architecture. The evaluation process involves analyzing the internal cohesion of clusters, examining the stability of clusters over time, and measuring the adaptability of clusters to changes in the software system.

The quality parameter evaluation serves as a crucial step in refining the recovered architecture and selecting the most suitable architectural representation among the generated cluster solutions.

4. Determination of Best Recovered Architecture: Based on the results of the quality parameter evaluation, the best solution is determined as the best recovered architecture. The solution that demonstrates high coherence, stability, and adaptability is selected as the final representation of the software architecture.

The selected architecture provides valuable insights into the organization, relationships, and behavior of software components within the system. It aids software developers, architects, and maintenance teams in comprehending the software's structure and interactions, leading to improved maintenance, evolution, and decision-making processes.

In one embodiment, the present invention offers a novel software architecture recovery approach that effectively combines various features, clustering techniques, and quality parameter evaluation to provide an accurate, coherent, and adaptable representation of software architecture. By leveraging structural, semantic, evolutionary, dynamic, and directory-based features, this approach addresses the complexities of modern software systems and enhances the understanding and maintenance of software architectures.

In yet another embodiment, the present invention holds significant potential for shaping the future of software engineering and architectural recovery. As technology continues to advance, the following future possibilities emerge:

1. Automated System Evolution: The SAR approach can be extended to automate the evolution of software systems. By continuously analyzing changes and adapting architectural representations, the invention could facilitate real-time adjustments, enhancing system robustness and agility.

2. AI-Enhanced Architectural Insights: Integration with advanced AI and machine learning techniques could lead to more intelligent architectural recovery. AI-driven algorithms could identify intricate dependencies and patterns, offering deeper insights into software behavior and potential optimizations.

3. Cross-Domain Application: The invention's adaptable approach could find application beyond traditional software systems, such as in IoT networks, cloud computing, and cyber-physical systems, providing a versatile solution for recovering complex architectures in diverse domains.

4. Collaborative Software Development: Collaboration tools could leverage the invention to enhance team communication and understanding. Developers from different backgrounds could better grasp the architectural structure, leading to improved collaboration and faster development cycles.

5. Predictive Maintenance: By integrating predictive analytics, the invention could forecast architectural weaknesses and potential areas of concern. This predictive maintenance approach could proactively prevent architectural degradation and system failures.

6. Enhanced Software Refactoring: The SAR approach could be integrated into software refactoring tools, guiding developers to make informed decisions during code restructuring, leading to improved code quality and maintainability.

7. Education and Training: The invention's comprehensive representation of software architectures could be used for educational purposes, providing students with hands-on experience in analyzing, understanding, and optimizing complex software systems.

8. Regulatory Compliance and Security: The invention's ability to accurately recover architectural dependencies could aid in regulatory compliance audits and security assessments, ensuring that systems adhere to required standards and best practices.

9. Integrated DevOps Practices: Integration with DevOps practices could streamline the deployment and maintenance of software systems, enabling more efficient continuous integration, continuous delivery, and continuous monitoring.

10. Holistic System Management: The invention's insights into software architecture could play a crucial role in holistic system management, helping organizations optimize resource allocation, enhance scalability, and make informed strategic decisions.

In yet another embodiment, the future possibilities of the invention are vast and extend beyond traditional software architecture recovery. With ongoing advancements in technology and the increasing complexity of software systems, the invention's adaptable and comprehensive approach has the potential to revolutionize how architectures are understood, maintained, and evolved across a wide range of applications and industries.

The present invention introduces an innovative approach for Software Architecture Recovery (SAR) that addresses the challenges of comprehending and maintaining complex software systems. The proposed method leverages structural, semantic, evolutionary, dynamic, and directory-based features of software elements to efficiently represent dependencies and interactions among them.

This innovative SAR approach enhances the understanding, maintenance, and evolution of complex software systems, providing valuable insights for architects, developers, and

maintenance teams. In a rapidly evolving technological landscape, the proposed method empowers effective decision-making and facilitates efficient software system management.

While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the invention and not as limitation. , Claims:1. A method for software architecture recovery comprising:
• Utilizing structural, semantic, evolutionary, dynamic, and/or directory-based features of software elements to represent dependencies among said elements efficiently.
• Performing clustering at the class level using an efficient soft computing approach.
• Evaluating recovered clusters using different quality parameters to assess the coherence and stability of the recovered architecture.
• Selecting the best recovered architecture based on the results of the quality parameter evaluation.

2. The method of claim 1, wherein the structural features include class relationships, inheritance hierarchies, and method invocations.

3. The method of claim 1, wherein the semantic features include semantic similarity between software elements.

4. The method of claim 1, wherein the evolutionary features include version history and change patterns of software elements.

5. The method of claim 1, wherein the dynamic features include runtime behavior and interactions among software elements.

6. The method of claim 1, wherein the directory-based features include organization and placement of software elements in a directory structure.

7. The method of claim 1, wherein the soft computing approach includes fuzzy clustering, neural network-based clustering, or genetic algorithm-based clustering.

8. The method of claim 1, wherein the quality parameters include cluster coherence, stability over time, and adaptability to changes.

9. A computer-readable medium containing instructions for performing the method of claim 1.

10. A system for software architecture recovery comprising:
• Means for utilizing structural, semantic, evolutionary, dynamic, and/or directory- based features of software elements to represent dependencies among said elements efficiently.
• Means for performing clustering at the class level using an efficient soft computing approach.
• Means for evaluating recovered clusters using different quality parameters to assess the coherence and stability of the recovered architecture.
• Means for selecting the best recovered architecture based on the results of the quality parameter evaluation.