An Exploratory Study of Software Sustainability at Early Stages of Software Development

Noman, Hira; Mahoto, Naeem Ahmed; Bhatti, Sania; Abosaq, Hamad Ali; Reshan, Mana Saleh Al; Shaikh, Asadullah

doi:10.3390/su14148596

Cited by 11 publications

(8 citation statements)

References 41 publications

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“…The definition of sustainability is multi-faceted 28,16,29,30,31,32,33 . Most prior studies focused on investigating the success of OSS projects 34,28,66 .…”

Section: Characterizing the Project Sustainabilitymentioning

confidence: 99%

“…In this regard, Valiev et al 16 and Zhang et al 17 all defined project sustainability from the perspective of commit frequency. Moreover, Noman et al 32 revealed that the technical (code-based) aspect of sustainability is considered most important by a majority of professionals. Since most relevant studies define sustainability from technical perspective and commit frequency.…”

Section: Characterizing the Project Sustainabilitymentioning

confidence: 99%

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On the Sustainability of Deep Learning Projects: Maintainers’ Perspective

Han

Liu

et al. 2023

Preprint

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Deep learning (DL) techniques have grown in leaps and bounds in both academia and industry over the past few years. Despite the growth of DL projects, there has been little study on how DL projects evolve, whether maintainers in this domain encounter a dramatic increase in workload, and whether or not existing maintainers can guarantee the sustained development of projects. To address this gap, we perform an empirical study to investigate the sustainability of DL projects, understand maintainers’ workloads and workloads growth in DL projects, and compare them with traditional OSS projects. In this regard, we first investigate how DL projects grow, then, understand maintainers’ workload in DL projects, and explore the workload growth of maintainers as DL projects evolve. After that, we mine the relationships between maintainers’ activities and the sustainability of DL projects. Eventually, we compare it with traditional OSS projects. Our study unveils that although DL projects show increasing trends in most activities, maintainers’ workloads present a decreasing trend. Meanwhile, the proportion of workload maintainers conducted in DL projects is significantly lower than in traditional OSS projects. Moreover, there are positive and moderate correlations between the sustainability of DL projects and the number of maintainers’ releases, pushes, and merged pull requests. Our findings shed lights that help understand maintainers’ workload and growth trends in DL and traditional OSS projects, and also highlight actionable directions for organizations, maintainers, and researchers.

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“…The definition of sustainability is multi-faceted 28,16,29,30,31,32,33 . Most prior studies focused on investigating the success of OSS projects 34,28,66 .…”

Section: Characterizing the Project Sustainabilitymentioning

confidence: 99%

Section: Characterizing the Project Sustainabilitymentioning

confidence: 99%

On the Sustainability of Deep Learning Projects: Maintainers’ Perspective

Han

Liu

et al. 2023

Preprint

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“…This paper focuses on sustainability aspects induced by computing, as proposed by [63][64][65][66]. These measurements focus on estimating the time and resource consumption, which allows us to determine the carbon footprint of the experimentation in an estimated but precise way.…”

Section: Sustainability Metricsmentioning

confidence: 99%

Improving Automated Machine-Learning Systems through Green AI

Castellanos-Nieves,

García-Forte

2023

Applied Sciences

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Automated machine learning (AutoML), which aims to facilitate the design and optimization of machine-learning models with reduced human effort and expertise, is a research field with significant potential to drive the development of artificial intelligence in science and industry. However, AutoML also poses challenges due to its resource and energy consumption and environmental impact, aspects that have often been overlooked. This paper predominantly centers on the sustainability implications arising from computational processes within the realm of AutoML. Within this study, a proof of concept has been conducted using the widely adopted Scikit-learn library. Energy efficiency metrics have been employed to fine-tune hyperparameters in both Bayesian and random search strategies, with the goal of enhancing the environmental footprint. These findings suggest that AutoML can be rendered more sustainable by thoughtfully considering the energy efficiency of computational processes. The obtained results from the experimentation are promising and align with the framework of Green AI, a paradigm aiming to enhance the ecological footprint of the entire AutoML process. The most suitable proposal for the studied problem, guided by the proposed metrics, has been identified, with potential generalizability to other analogous problems.

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“…For example, environmentally sustainable software requires less energy and resources and provides a smaller IT system carbon footprint; economically sustainable software optimizes costs and resource allocation and extends the hardware life cycle, yielding long-term benefits for enterprises; and socially sustainable software encourages developers, end-users, and other stakeholders to embrace responsible software practices that support sustainability goals [20], [21].…”

Section: Sustainable Procurementmentioning

confidence: 99%

“…Environmental Sustainability "Environmental software sustainability" involves developing and maintaining software systems that minimize ecological effects, promote resource efficiency, and ensure long-term software viability. For instance, energy-efficient algorithms and data storage methods reduce energy consumption [21]. • Social Sustainability "Social software sustainability" is the design, development, and maintenance of software systems that promote positive social effects, inclusivity and accessibility, and long-term benefits to users and communities, such as a collaborative platform that promotes equal access to information and learning resources for people of all socioeconomic backgrounds [22].…”

mentioning

confidence: 99%

BERT-Based Approach for Greening Software Requirements Engineering Through Non-Functional Requirements

Subahi

2023

IEEE Access

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The incorporation of sustainability principles during the requirements engineering phase of the development life cycle constitutes greening software requirements. This incorporation can have a variety of effects on the software design employed in modern and cutting-edge information technology (IT) systems. When sustainability principles are incorporated into requirements engineering, software design priorities can change and address current design issues such as energy and resource consumption, modularity, maintainability, and adaptability. In contrast to other green approaches that consider sustainable development, there is a further need to investigate the relationship between software development and the relevant green principles of sustainability during the requirements engineering phase. We present a new mechanism for mapping software nonfunctional requirements (NFRs) to defined dimensions of green software sustainability, consisting of two mapping steps: 1) between NFRs and sustainability dimensions; and 2) between sustainability dimensions and two clusters of green IT aspects defined in this work. The overall architecture of the promising approach is based on the use of the Bidirectional Encoder Representations from Transformers (BERT) language model with an expanded dataset. We consider transfer learning and domainspecific fine-tuning capabilities for constructing and evaluating a model specifically tailored for developing a proof of concept of the greening software requirements engineering task, as language models have recently emerged as a potent technique in the field of software engineering, with numerous applications in code analysis, automated documentation, and code generation. In addition, we test the model's performance using an extended version of the PROMISE_exp dataset after adding a new binary classification column for categorizing sustainability dimensions into two defined clusters: Eco-technical and Socioeconomic, and having a selected domain expert label the raw data. The model's efficiency is evaluated using four matrices-1) accuracy; 2) precision; 3) recall; and 4) F1 score-across a variety of epoch and batch sizes. Our numerical results demonstrate the viability of the approach in text classification tasks via performing well in mapping NFRs to software sustainability dimensions. This acts as a proof of concept for automating the sustainability measurement of software awareness at the early development stage. In addition, the results emphasize the importance of domain-specific fine-tuning and transfer learning for obtaining high performance in classification tasks in requirements engineering.

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An Exploratory Study of Software Sustainability at Early Stages of Software Development

Cited by 11 publications

References 41 publications

On the Sustainability of Deep Learning Projects: Maintainers’ Perspective

On the Sustainability of Deep Learning Projects: Maintainers’ Perspective

Improving Automated Machine-Learning Systems through Green AI

BERT-Based Approach for Greening Software Requirements Engineering Through Non-Functional Requirements

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