Sustainable Software Development Research Articles

A Proposal of Behavior-Based Consumption Profiles for Green Software Design

Despite the increase in the efficiency of energy consumption in information and communication technology, software execution and its constraints are responsible for how energy is consumed in hardware hosts. Consequently, researchers have promoted the development of sustainable software with new development methods and tools to lessen its hardware demands. However, the approaches developed so far lack cohesiveness along the stages of the software development life cycle (SDLC) and exist outside of a holistic method for green software development (GSD). In addition, there is a severe lack of approaches that target the analysis and design stages of the SDLC, leaving software architects and designers unsupported. In this article, we introduce our behavior-based consumption profile (BBCP) external Domain-Specific Language (DSL), aimed at assisting software architects and designers in modeling the behavior of software. The models generated with our external DSL contain multiple sets of properties that characterize features of the software’s behavior. In contrast to other modeling languages, our BBCP emphasizes how time and probability are involved in software execution and its evolution over time, helping its users to gather an expectation of software usage and hardware consumption from the initial stages of software development. To illustrate the feasibility and benefits of our proposal, we conclude with an analysis of the model of a software service created using the BBCP, which is simulated using Insight Maker to obtain an estimation of hardware consumption and later translated to energy consumption.

GBasis: A Python library for evaluating functions, functionals, and integrals expressed with Gaussian basis functions.

GBasis is a free and open-source Python library for molecular property computations based on Gaussian basis functions in quantum chemistry. Specifically, GBasis allows one to evaluate functions expanded in Gaussian basis functions (including molecular orbitals, electron density, and reduced density matrices) and to compute functionals of Gaussian basis functions (overlap integrals, one-electron integrals, and two-electron integrals). Unique features of GBasis include supporting evaluation and analytical integration of arbitrary-order derivatives of the density (matrices), computation of a broad range of (screened) Coulomb interactions, and evaluation of overlap integrals of arbitrary numbers of Gaussians in arbitrarily high dimensions. For circumstances where the flexibility of GBasis is less important than high performance, a seamless Python interface to the Libcint C package is provided. GBasis is designed to be easy to use, maintain, and extend following many standards of sustainable software development, including code-quality assurance through continuous integration protocols, extensive testing, comprehensive documentation, up-to-date package management, and continuous delivery. This article marks the official release of the GBasis library, outlining its features, examples, and development.

Practical Sustainable Software Development in Architectural Flexibility for Energy Efficiency Using the Extended Agile Framework

Many regular business operations are transforming into digital services, increasing advanced multi-platforms, rapid operational alignment, flexibility, and environmental impact through energy consumption, hardware waste, and technology investments. Flexible and sustainable system development models emphasizing energy efficiency can help innovate software development as digital servicing applications shift. This research is motivated by the need to improve energy consumption in early software design and development due to rising technological efficiency and sustainability demands. Although effective in iterative development and stakeholder engagement, traditional Agile methodologies often struggle with long-term sustainability and energy efficiency. Extended Agile, combining Agile, layered architecture, and aspect-oriented frameworks (ALAI), promises to improve system modularity, flexibility, maintainability, and sustainability. This study’s findings are not just theoretical, but also practically relevant, as they explore the energy efficiency of ALAI software development methodologies, using graduate admission information system services (GAISS) as an example. GAISS is a complex system that handles the entire process of graduate admissions, from application submission to final decision. The study quantifies the energy usage of a student-list webpage by analyzing Microsoft IIS server logs from February 2022 to May 2024. Directly applicable findings show that the GAISS based on the ALAI framework reduces energy consumption by 10.7914% compared to traditional Agile software developments. ALAI used 892.80 kWh versus Agile’s 1000.80 kWh during operations, saving energy. These findings demonstrate the benefits of integrating aspect-oriented frameworks and layering approaches into Agile methodologies, contributing to sustainable software development discourse. The study emphasizes the importance of energy-efficient frameworks such as ALAI to reduce software systems’ environmental impact and promote software development sustainability. The findings of this study, with their practical relevance, assist software developers and organizations in choosing software design and development methods that maximize operational efficiency and environmental sustainability.

Sustainable Software Development: A Scrum and Kanban Approach

This The ever-increasing demand for robust software solutions requires continuous improvements in software development techniques. Despite the tremendous positive impact software has on society, it is important not to overlook its negative impact on the environment. With climate change becoming more apparent, it is important to understand and address the environmental impacts of software development.This research seeks to explore ways to integrate sustainable development into existing software development methodologies, with a particular focus on Scrum and Kanban. The research basically consists of two parts: a comprehensive review of existing literature to identify solutions and guidelines for sustainable software development,followed by interviews with developers and managers using Scrum and Kanban methodologies on a regular basis. The goal is to assess the sustainability of their practices and identify opportunities for improvement. Through a literature review, a taxonomy of key characteristics recommended for consideration throughout the software development process was established. Combining these factors with the insights gleaned from the interviews, the study aims to propose the enhancement of software-related planning and development within the framework of Scrum and Kanban, thereby providing factors a sustainability has improved.

Tools, techniques, and trends in sustainable software engineering: A critical review of current practices and future directions

The quest for sustainability has extended its reach into the realm of software engineering, prompting an exploration of tools, techniques, and emerging trends to mitigate the environmental impact of software development and operation. This review provides a critical review of current practices and future directions in sustainable software engineering. In recent years, the software industry has recognized the need to address the environmental footprint of software systems, considering factors such as energy consumption, resource utilization, and carbon emissions. Consequently, a plethora of tools and techniques have emerged to support sustainable software development processes. These range from energy-efficient programming languages and frameworks to eco-friendly software architectures and design patterns. Moreover, methodologies such as Green Software Engineering (GSE) and Sustainable Software Development (SSD) have gained traction, emphasizing the integration of sustainability considerations throughout the software development lifecycle. By adopting practices like green requirements engineering, energy-aware testing, and eco-design principles, organizations can optimize their software systems for reduced environmental impact without compromising functionality or performance. Furthermore, trends in sustainable software engineering extend beyond traditional development practices. The rise of cloud computing, edge computing, and Internet of Things (IoT) technologies presents both challenges and opportunities for sustainability. Techniques such as serverless computing and containerization offer potential benefits in terms of resource efficiency and scalability, while also introducing new considerations regarding data center energy consumption and electronic waste management. Looking ahead, the future of sustainable software engineering is marked by innovation and collaboration. Emerging technologies such as artificial intelligence (AI) and blockchain hold promise for optimizing resource allocation, enhancing energy efficiency, and fostering transparency in sustainability efforts. Additionally, interdisciplinary collaboration between software engineers, environmental scientists, and policymakers will be essential in shaping a more sustainable digital ecosystem. The journey towards sustainable software engineering involves a multifaceted approach encompassing tools, techniques, and ongoing adaptation to evolving trends. By critically evaluating current practices and embracing future directions, the software industry can contribute to a more environmentally responsible and resilient future.

A CASE environment for Project-based Course to learn a sustainable software development

In the context of contemporary software development education, project-based courses have gained prominence as effective pedagogical tools. This methodology empowers students to cultivate practical skills by engaging in real-world projects. However, integrating sustainable software development practices into such courses often remains a challenge such as the need to set up new technologies for each semester, making it hard to ensure consistent and disciplined development. To address this gap, this research introduces Agilearn, the conception of a Computer-Aided Software Engineering (CASE) environment tailored explicitly for project-based courses with a strong focus on promoting sustainability. It is expected to enhance the learning experience by offering a collaborative platform that automates tasks and establishes a standardized agile environment for students. This paper presents the requirements analysis, the course design to implement Agilearn, the architecture of Agilearn, and a case study to implement Agilearn.

Automatic detection of Feature Envy and Data Class code smells using machine learning

Code smells in software indicate poor design and implementation choices. Detecting and removing them is critical for sustainable software development. Machine learning (ML) can automate code smell detection. Most ML solutions train models from scratch on code smell datasets, using handcrafted source code metrics as features. Pretrained language models, like BERT, fueled a paradigm shift in natural language processing: from handcrafted features to automatically inferred features and from training models from scratch to using pretrained models. Code embeddings offer the potential to bring a similar paradigm shift to code analysis. Nevertheless, the potential of using pretrained neural code embeddings for code smell detection has yet to be fully explored. To this end, we evaluated ML models trained using different code representations: code metrics and state-of-the-art neural code embeddings (CodeT5 and CuBERT). We experimented with CodeT5 variants (base and small) and explored multiple ways of embedding code snippets (by combining line-level embeddings or passing the entire code snippet as input). We tested our approaches on the tasks of detecting Data Class and Feature Envy on the MLCQ dataset. Considering the results of this study and our previous research, performance-wise, there is no clear winner between using code metrics or code embeddings for different code smell types and programming languages. However, given that, in contrast to code metrics, code embeddings can automatically adapt to new programming constructs and are expected to scale better with dataset size, these models are likely to become the future state-of-the-art feature generation technique for code smell detection.

A machine learning approach to predict DevOps readiness and adaptation in a heterogeneous IT environment

Software and information systems have become a core competency for every business in this connected world. Any enhancement in software delivery and operations will tremendously impact businesses and society. Sustainable software development is one of the key focus areas for software organizations. The application of intelligent automation leveraging artificial intelligence and cloud computing to deliver continuous value from software is in its nascent stage across the industry and is evolving rapidly. The advent of agile methodologies with DevOps has increased software quality and accelerated its delivery. Numerous software organizations have adopted DevOps to develop and operate their software systems and improve efficiency. Software organizations try to implement DevOps activities by taking advantage of various expert services. The adoption of DevOps by software organizations is beset with multiple challenges. These issues can be overcome by understanding and structurally addressing the pain points. This paper presents the preliminary analysis of the interviews with the relevant stakeholders. Ground truths were established and applied to evaluate various machine learning algorithms to compare their accuracy and test our hypothesis. This study aims to help researchers and practitioners understand the adoption of DevOps and the contexts in which the DevOps practices are viable. The experimental results will show that machine learning can predict an organization's readiness to adopt DevOps.

Futurology

Abstract Measuring software engineering efficiency has always been an impossible task. Matt Lucas FBCS reflects on the expansion of environmental, social and governance reporting requirements and what they mean for understanding sustainable software development.

Green Software Engineering: Cloud-based Face Detection and Static Code Analysis

This paper presents an approach to green software engineering that integrates cloud-based face detection and static code analysis to promote sustainable software development. The proposed method uses OpenCV, a computer vision library, and a pre-trained Haar cascade classifier to detect faces in images. Faces are marked with green bounding frames that serve as visual indicators of their locations. In addition, the paper evaluates the quality of a distinct script file using Pylint library static code analysis techniques. The analysis evaluates code compliance with standards, identifies potential flaws, and identifies code odors. By integrating these practices, the proposed method seeks to reduce resource consumption, maximize energy efficiency, and enhance code maintainability, promoting environmentally friendly and sustainable software engineering practices. One outcome of our effort was creating the YasminNadiaArabcSocialMediaImages data collection, which includes faces of Arabic social media celebrities and is filled out to be accessible for public usage on the websites Kaggle and GitHub.

GreenPy: Evaluating Application-Level Energy Efficiency in Python for Green Computing

The increased use of software applications has resulted in a surge in energy demand, particularly in data centers and IT infrastructures. As global energy consumption is projected to surpass supply by 2030, the need to optimize energy consumption in programming has become imperative. Our study explores the energy efficiency of various coding patterns and techniques in Python, with the objective of guiding programmers to a more informed and energy-conscious coding practices. The research investigates the energy consumption of a comprehensive range of topics, including data initialization, access patterns, structures, string formatting, sorting algorithms, dynamic programming and performance comparisons between NumPy and Pandas, and personal computers versus cloud computing. The major findings of our research include the advantages of using efficient data structures, the benefits of dynamic programming in certain scenarios that saves up to 0.128J of energy, and the energy efficiency of NumPy over Pandas for numerical calculations. Additionally, the study also shows that assignment operator, sequential read, sequential write and string concatenation are 2.2 times, 1.05 times, 1.3 times and 1.01 times more energy-efficient choices, respectively, compared to their alternatives for data initialization, data access patterns, and string formatting. Our findings offer guidance for developers to optimize code for energy efficiency and inspire sustainable software development practices, contributing to a greener computing industry.

Agile Software Requirements Engineering Challenges-Solutions—A Conceptual Framework from Systematic Literature Review

Agile software requirements engineering processes enable quick responses to reflect changes in the client’s software requirements. However, there are challenges associated with agile requirements engineering processes, which hinder fast, sustainable software development. Research addressing the challenges with available solutions is patchy, diverse and inclusive. In this study, we use a systematic literature review coupled with thematic classification and gap mapping analysis to examine extant solutions against challenges; the typologies/classifications of challenges faced with agile software development in general and specifically in requirements engineering and how the solutions address the challenges. Our study covers the period from 2009 to 2023. Scopus—the largest database for credible academic publications was searched. Using the exclusion criteria to filter the articles, a total of 78 valid papers were selected and reviewed. Following our investigation, we develop a framework that takes a three-dimensional view of agile requirements engineering solutions and suggest an orchestrated approach balancing the focus between the business context, project management and agile techniques. This study contributes to the theoretical frontier of agile software requirement engineering approaches and guidelines for practice.

OntoSuSD: Software engineering approaches integration ontology for sustainable software development

AbstractDifferent 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.

Data Flows for Metabarcoding-based Monitoring in the Project Automated Multisensor Stations for Monitoring of Biodiversity

Biodiversity monitoring is an important tool to document rapid ecosystem changes and on-going species loss by collecting high-resolution biodiversity data over long periods of time. Large-scale monitoring provides long-range information on species occurrence, interactions, and migrations. In the German project "Automated Multisensor Stations for Monitoring of BioDiversity" (AMMOD, https://ammod.de/), field stations equipped with different sensors (e.g. acoustic sensors, camera traps, volatile organic compound sensors, pollen and insect traps) are being developed that can be operated largely autonomously. These stations provide continuous data on the occurrence of birds, bats, mammals, insects and plants and associated environmental parameters. Besides the autonomous operation of the stations, a challenge is to master the different requirements that the wide range of sensors bring with them (cf. Wägele et al. 2022). In addition to the detection of fauna and flora by means of sounds, images, films and smells, samples are also collected in malaise and pollen traps with automatically rotating multi samplers. The mixed samples are processed in the laboratory using metabarcoding methods and the resulting sequence data bioinformatically processed into amplicon sequence variants (ASV, Callahan et al. 2017). Unlike data from acoustic sensors or camera traps, which can flow and be analyzed almost entirely automatically, metabarcoding requires a labor-intensive intermediate step in the laboratories at the University of Kassel (pollen) and the LIB (insects). In addition, metabarcoding requires good (preferably local) barcode reference databases, like Barcode Of Life Data system (BOLD) and the German Barcode of Life library (GBOL); long-term data storage for raw and processed data; reliable retrieval of ASV sequences; and versioning of supercharged data through dynamic assignment during taxon annotation. Here, we present the pipeline for acquisition, intersection, management, storage and publication of data from metabarcoding derived from bulk insect and pollen samples. The metabarcoding data pipeline is connected to the overall data flow developed in the AMMOD project (Wägele et al. 2022, Fig. 12) and ultimately delivers processed taxon tables with linked information to the raw sequences published, methods used and other sources of information to the upcoming NFDI4Biodiversity Research Data Commons (RDC, https://www.nfdi4biodiversity.org, Glöckner et al. 2020) using the AMMOD data broker. Raw sequences (i.e., FASTQ files, sampling and laboratory metadata) will be stored and published the in the International Nucleotide Sequence Database Collaboration (GenBank or ENA) using the data flows from the German Federation for Biological Data (GFBio, https://gfbio.org) as proxy. The three data sources for the AMMOD metabarcoding data pipeline are derived from the multisampler with connected malaise or spore traps plus environmental data loggers. Bioinformatic analysis and generation of ASVs is provided by the users, using their own pipelines. For storage, publication and transmission of results to the NFDI4Biodiversity RDC, an ASV table registry is provided along with the possibility of uploading log files, laboratory protocols and collection tables. In the ASV registry, the uploaded tables are taxonomically annotated through the connected reference databases and stored as versioned datasets. The processed and annotated datasets are transmitted to the AMMOD broker in a standardized format as JSON datasets. Connection of the components: an identifier, which uniquely identifies the data from the respective components of field stations and laboratories, is the prerequisite for the unique assignment of data parts. This AMMOD ID consists of the project name, the case number, the sensor type, a time stamp with the number of the sample and the bottle or plate number. For example, the ID AMMOD-T1-M-R210511-B1 denotes a sample of the AMMOD project from trap 1 of type Malaise dated May 11, 2021 from the first bottle. All bottles and data entries from the laboratory, log files and subsequent processes are provided with such an ID. The orchestra of different components was built under the aspect of sustainable software development and compatibility to existing systems such as the Leibniz Institute for the Analysis of Biodiversity Change (LIB) Biobank and the LIB Digital Collection Catalog. All developed software is published at: https://gitlab.leibniz-lib.de/AMMOD/ and https://gitlab.leibniz-lib.de/GBOL/asv-table-registry The portal for ASV registration and upload is available on: https://bolgermany.de/metabarcoding

Towards a classification of sustainable software development process using manifold machine learning techniques

With the evaluation of the software industry, a huge number of software applications are designing, developing, and uploading to multiple online repositories. To find out the same type of category and resource utilization of applications, researchers must adopt manual working. To reduce their efforts, a solution has been proposed that works in two phases. In first phase, a semantic analysis-based keywords and variables identification process has been proposed. Based on the semantics, designed a dataset having two classes: one represents application type and the other corresponds to application keywords. Afterward, in second phase, input preprocessed dataset to manifold machine learning techniques (Decision Table, Random Forest, OneR, Randomizable Filtered Classifier, Logistic model tree) and compute their performance based on TP Rate, FP Rate, Precision, Recall, F1-Score, MCC, ROC Area, PRC Area, and Accuracy (%). For evaluation purposes, We have used an R language library called latent semantic analysis for creating semantics, and the Weka tool is used for measuring the performance of algorithms. Results show that the random forest depicts the highest accuracy which is 99.3% due to its parametric function evaluation and less misclassification error.

Guidelines for collaborative development of sustainable data treatment software

Software development for data reduction and analysis at large research facilities is increasingly professionalized, and internationally coordinated. To foster software quality and sustainability, and to facilitate collaboration, representatives from software groups of European neutron and muon facilities have agreed on a set of guidelines for development practices, infrastructure, and functional and non-functional product properties. These guidelines have been derived from actual practices in software projects from the EU funded consortium ‘Science and Innovation with Neutrons in Europe in 2020’ (SINE2020), and have been enriched through extensive literature review. Besides guiding the work of the professional software engineers in our computing groups, we hope to influence scientists who are willing to contribute their own data treatment software to our community. Moreover, this work may also provide inspiration to scientific software development beyond the neutron and muon field.

Investigating the Higher Education Curriculum for Sustainable Software Development

Investigating the Higher Education Curriculum for Sustainable Software Development

Green-Agile Maturity Model: An Evaluation Framework for Global Software Development Vendors

Agile methods are extensively adapted by software development organizations due to the competitive benefits it offers. In recent years global software development (GSD) projects practice agile methods as prominent methods to deliver the software in increments with utmost user satisfaction and affordable cost. Beside the use of agile methods, the software industry has also considered the green aspect of software, to be in line with the demands of the organizations and the world technological ecosystem. The green and sustainable feature of software should focus both the energy and resource efficiency key factors. This phenomenon of embedding the green flavor in software has emerged a new research area, green software engineering, that promises the development of eco-friendly software with minimum energy and use of less computing resources, to trim down the adverse effects on both society and environment. The principal objective of this research study is to design and develop a multi-level Green-Agile Maturity Model (GAMM) to assess the GSD vendors’ agile maturity in terms of green software development. The model has been built in four phases. In phase I and II, systematic literature review (SLR) was performed to identify the success factors and risk factors that either supports or hinders the green and sustainable software development respectively by practicing the agile methods in GSD. The results have been validated from 106 relevant experts, dealing with agile and green software projects, through questionnaire survey. The experts’ demographic represents 25 different countries. We also identified the industry practices through SLR and survey, to address our identified critical factors. Phase III of this research deals with development of the GAMM by categorizing the identified factors into seven Green-Agile maturity levels. A similar approach has been used in other models such as Capability Maturity Model Integration (CMMI), Implementation Maturity Model (IMM) and Software Outsourcing Vendors Readiness Model (SOVRM). In phase IV of this research, five case studies were conducted at GSD organizations, to evaluate the structure and efficacy of the GAMM, while as a major contribution, this paper presents our developed model, the GAMM, which aims to assess the green-agile maturity of the GSD vendors in terms of green and sustainable software development.

Development of services in the field of research data management and scholarly communication at the University of Vienna

In 2008 the University of Vienna released the institutional repository PHAIDRA. With this release, several services for research data management followed soon. It is still essential to review the services and integrate new one, related to the change of the research practice. The demand for training and support is increasing, and more often, we see now the need for consulting services. That service supports the research cycle from writing a data management plan to getting DOIs for data and are integrated into the PHAIDRA portfolio. PHAIDRA is a cooperation between the Central University Computer Center and the University Library for researcher and research projects of the University of Vienna. To be alleged with the actual changes in research data management, PHAIDRA regularly participates in national and international projects, like Austrian Data Lab and Services and FAIR Data Austria nationally as well as implementing with COAR (Confederation of Open Access Repositories) a vocabulary service and engaging with researcher projects together with RDA (Research Data Alliance) internationally. Several networks such as the network for repository managers (RepManNet) or the network for sustainable software development (code4research), and the network of other PHAIDRA users at other universities in Austria, Italy and Balkan countries, enable constructive cooperation on a technical, library and scientific level on various topics. The poster intends to give an insight into these activities.

A Community of Practice Around Peer Review for Long-Term Research Software Sustainability

Scientific open source projects are responsible for enabling many of the major advances in modern science including recent breakthroughs such as the Laser Interferometer Gravitational-Wave Observatory project recognized in the 2017 Nobel Prize for physics. However, much of this software ecosystem is developed ad hoc with no regard for sustainable software development practices. This problem is further compounded by the fact that researchers who develop software have little in the way of resources or academic recognition for their efforts. The rOpenSci Project, founded in 2011 with the explicit mission of developing software to support reproducible science, has in recent years undertaken an effort to improve the long tail of scientific software. In this paper, we describe our software peer-review system, which brings together the best of traditional academic review with new ideas from industry code review.