ABSTRACT The demand for sustainable digital and information systems requires a transformative shift in software engineering practices that balance technical performance with economic, environmental, and social responsibility. This paper introduces LAGSSE, which is a novel framework that integrates Lean, Agile, and Green principles to advance Sustainable Software Engineering (SSE). LAGSSE operationalizes six interconnected sustainability dimensions: technical, environmental, economic, social, process, and infrastructure through a layered architecture and lifecycle‐based approach, underpinned by the OntoSuSD ontology for semantic clarity and traceability. A two‐phase validation strategy confirms the framework's strength; that is, a Delphi‐based expert study ( n = 75) establishes conceptual clarity and applicability, while simulation‐based evaluations demonstrate measurable improvements in energy efficiency (25%), defect reduction (33%), deployment speed (30%), and stakeholder satisfaction (24%) compared to baseline Agile practices. Visual models, statistical charts, and a Sustainability Performance Score (SPS) equation reinforce the framework's empirical grounding and practical utility. This framework not only addresses critical gaps in existing SSE frameworks but also provides an adaptable, comprehensive, and validated pathway for embedding sustainability into software engineering education, industry practice, and policy development.

Abstract Different software development approaches (SDAs) are developed with broad portfolios of development processes. Each of the approaches has certain exclusive principles, practices, thinking, and values, which are informally represented, implemented, and improperly institutionalized. Ontologies are developed for the representation, assessment, and adaptation of SDAs separately without having a shared terminology which may lead to terminological conflict and confusion affecting the simultaneous representation and implementation in software development industry and academia. The software engineering approaches does not consider and support sustainability as priority concern. However, the approaches have capabilities of supporting sustainable software development in different sustainability aspects. This research article aims for the designing and development of an integrated ontology of software engineering approaches (i.e., agile, lean, and green) named OntoSuSD (ontology for sustainable software development) to support sustainable software development knowledge, awareness, and implementation. The goal of OntoSuSD is to propose, design and develop a formal, generic, consistent, and shared knowledge base containing semantic terminology and description of concepts and relationships generated around the representation and implementation of lean, agile, and green approaches in software development processes, which will facilitate their simultaneous implementation and assessment for sustainable software development. The OntoSuSD is developed using practical ontology engineering methodology by reusing relevant ontologies and explicit concepts and properties are defined to fulfill knowledge requirements and representations of the domain. The OntoSuSD is evaluated, and results infer OntoSuSD has high ontological design, good domain coverage, potential applications and achieves purpose of the ontology development.

Abstract An effective feature representation can boost recognition tasks in the sketch domain. Due to an abstract and diverse structure of the sketch relatively with a natural image, it is complex to generate a discriminative features representation for sketch recognition. Accordingly, this article presents a novel scheme for sketch recognition. It generates a discriminative features representation as a result of integrating asymmetry essential information from deep features. This information is kept as an original feature‐vector space for making a final decision. Specifically, five different well‐known pre‐trained deep convolutional neural networks (DCNNs), namely, AlexNet, VGGNet‐19, Inception V3, Xception, and InceptionResNetV2 are fine‐tuned and utilised for feature extraction. First, the high‐level deep layers of the networks were used to get multi‐features hierarchy from sketch images. Second, an entropy‐based neighbourhood component analysis was employed to optimise the fusion of features in order of rank from multiple different layers of various deep networks. Finally, the ranked features vector space was fed into the support vector machine (SVM) classifier for sketch classification outcomes. The performance of the proposed scheme is evaluated on two different sketch datasets such as TU‐Berlin and Sketchy for classification and retrieval tasks. Experimental outcomes demonstrate that the proposed scheme brings substantial improvement over human recognition accuracy and other state‐of‐the‐art algorithms.

Feature-based software birthmark is an essential property of software that can be used for the detection of software theft and many other purposes like to assess the security in end-to-end communication systems. Research on feature-based software birthmark shows that using the feature-based software birthmark joint with the practice of software birthmark estimation together can deliver a right and influential method for detecting software piracy and the amount of piracy done by a software. This can also guide developers in improving security of end-to-end communication system. Modern day software industry and systems are in demand to have an unbiased method for comparing the features-based birthmark of software competently, and more concretely for the detecting software piracy and assessing the security of end-to-end communication systems. In this paper, we proposed a mathematical model, which is based on a differential system, to present feature-based software birthmark. The model presented in this paper provides an exclusive way for the features-based birthmark of software and then can be used for comparing birthmark and assessing security of end-to-end communication systems. The results of this method show that the proposed model is efficient in terms of effectiveness and correctness for the features-based software birthmark comparison and security assessment purposes.

Various binary similarity measures have been employed in clustering approaches to make homogeneous groups of similar entities in the data. These similarity measures are mostly based only on the presence or absence of features. Binary similarity measures have also been explored with different clustering approaches (e.g., agglomerative hierarchical clustering) for software modularization to make software systems understandable and manageable. Each similarity measure has its own strengths and weaknesses which improve and deteriorate the clustering results, respectively. We highlight the strengths of some well-known existing binary similarity measures for software modularization. Furthermore, based on these existing similarity measures, we introduce several improved new binary similarity measures. Proofs of the correctness with illustration and a series of experiments are presented to evaluate the effectiveness of our new binary similarity measures.

The popularity of consuming multimedia content on mobile phones is increasing more and more, not only because of the availability of the technical infrastructure, but also because of the mobility in modern society. We are developing a mobile multimedia streaming application. The crucial factor for such applications in order to be adopted and successful is user acceptance. This paper presents the preliminary concept and design of a contextual mobile multimedia content usability study. The study is conducted within are search project on agile software development methodologies with special emphasis on Extreme Programming and continuous usability evaluation. Past work included satisfaction of the needs of end users by means of focusing on user-experience in all steps of the development process. To gain scientific relevant data, the careful design of a study is considered most important. The study which will be conducted in October 2008 will give insights into mobile HCI concerning the coherence of content types, consumption times, and consumption contexts.

Mobile phones have become full-featured mobile computers. Applications providing good user experience and taking full advantage of the increasing capabilities of mobile phones are still rare. One such application is audio and video on mobile phones which is expected to become a killer application in the near future. A lot of valuable audio and video content is hidden in archives of content providers. We are developing an application that enables a user to perform content-based search for audio and video content in large databases and play it on a mobile phone virtually anywhere, at any time. Our approach to application development focuses on the adoption of agile software development methodologies and user-centered design, emphasizing iterative user-interface development involving usability engineers and non-technical users. Thus, the application evolves according to the needs of the end user, providing maximized usability and customer satisfaction.