{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/software-engineering-challenges-of-deep","title":"Software Engineering Challenges of Deep Learning","arxiv_id":"1810.12034","date":"2018-10-29","proceeding":null,"authors":["Anders Arpteg","Björn Brinne","Luka Crnkovic-Friis","Jan Bosch"],"abstract":"Surprisingly promising results have been achieved by deep learning (DL)\nsystems in recent years. Many of these achievements have been reached in\nacademic settings, or by large technology companies with highly skilled\nresearch groups and advanced supporting infrastructure. For companies without\nlarge research groups or advanced infrastructure, building high-quality\nproduction-ready systems with DL components has proven challenging. There is a\nclear lack of well-functioning tools and best practices for building DL\nsystems. It is the goal of this research to identify what the main challenges\nare, by applying an interpretive research approach in close collaboration with\ncompanies of varying size and type.\n  A set of seven projects have been selected to describe the potential with\nthis new technology and to identify associated main challenges. A set of 12\nmain challenges has been identified and categorized into the three areas of\ndevelopment, production, and organizational challenges. Furthermore, a mapping\nbetween the challenges and the projects is defined, together with selected\nmotivating descriptions of how and why the challenges apply to specific\nprojects.\n  Compared to other areas such as software engineering or database\ntechnologies, it is clear that DL is still rather immature and in need of\nfurther work to facilitate development of high-quality systems. The challenges\nidentified in this paper can be used to guide future research by the software\nengineering and DL communities. Together, we could enable a large number of\ncompanies to start taking advantage of the high potential of the DL technology.","url_abs":"http://arxiv.org/abs/1810.12034v1","url_pdf":"http://arxiv.org/pdf/1810.12034v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"software-engineering-challenges-of-deep","repo_url":"https://github.com/HemingwayLee/data-collection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}