- author = "Haozhou Lyu and Gregory Gay and Maiko Sakamoto",
- title = "Exploring Genetic Improvement of the Carbon Footprint of Web Pages",
- booktitle = "SSBSE 2023",
- year = "2023",
- editor = "Paolo Arcaini and Tao Yue and Erik Fredericks",
- organisers = "Erik Fredericks and Paolo Arcaini and Tao Yue and Rebecca Moussa and Thomas Vogel and Gregory Gay and Max Hort and Bobby R. Bruce and Jose Miguel Rojas and Vali Tawosi",
- volume = "14415",
- series = "LNCS",
- pages = "67--83",
- address = "San Francisco, USA",
- month = "8 " # dec,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, genetic improvement, SBSE, Energy Consumption, Carbon footprint, Green AI, Software engineering, Case study, Thematic analysis, Web development",
- isbn13 = "978-3-031-48795-8",
-
URL = "
https://greg4cr.github.io/pdf/23cfgi.pdf",
-
DOI = "
doi:10.1007/978-3-031-48796-5_5",
- size = "15 pages",
- abstract = "we explore automated reduction of the carbon footprint of web pages through genetic improvement, a process that produces alternative versions of a program by applying program transformations intended to optimize qualities of interest. We introduce a prototype tool that imposes transformations to HTML, CSS, and JavaScript code, as well as image resources, that minimize the quantity of data transferred and memory usage while also minimizing impact to the user experience (measured through loading time and number of changes imposed). In an evaluation, our tool outperforms two baselines: the original page and randomized changes, in the average case on all projects for data transfer quantity, and 80% of projects for memory usage and load time, often with large effect size. Our results illustrate the applicability of genetic improvement to reduce the carbon footprint of web components, and offer lessons that can benefit the design of future tools.",
-
notes = "See also MSc http://hdl.handle.net/20.500.12380/306723
https://odr.chalmers.se/items/c832b3a9-609d-40af-bc39-b96278686042
co-located with ESEC/FSE 2023. https://conf.researchr.org/home/ssbse-2023",
