Metrics Driven Reengineering and Continuous Code Improvement at Meta
This program is tentative and subject to change.
The focus on rapid software delivery inevitably results in the accumulation of technical debt, which, in turn, affects quality and slows future development. Our primary aim is to discover how companies keep their codebases maintainable and how code improvements might be automated. Method: we investigate Meta practices by collaborating with engineers on code quality (via action research) and by analyzing rich source code change history using mixed-methods to reveal a range of practices used for continual improvement of the codebase. Results: Code improvements at Meta range from completely organic grass-roots done at the initiative of individual engineers, to regularly blocked time and engagement via gamification of Better Engineering (BE) work, to major explicit initiatives aimed at reengineering the complex parts of the codebase or deleting accumulations of dead code. Over 14% of changes are explicitly devoted to code improvement and the developers are given “badges” to acknowledge the type of work and the amount of effort. Based on the interactions with development teams we suggest metrics to help prioritization of code improvement efforts. Finally, our models of the impact of reengineering activities revealed substantial improvements in quality and speed and reductions in code complexity. Overall, code improvement activities are relatively effort intensive yet simple enough to be prime targets for automation.
This program is tentative and subject to change.
Tue 18 NovDisplayed time zone: Seoul change
16:00 - 17:00 | |||
16:00 10mTalk | An Empirical Study on UI Overlap in OpenHarmony Applications Industry Showcase | ||
16:10 10mTalk | Metrics Driven Reengineering and Continuous Code Improvement at Meta Industry Showcase Audris Mockus University of Tennessee, Peter C Rigby Meta / Concordia University, Rui Abreu Meta, Nachiappan Nagappan Meta Platforms, Inc. | ||
16:20 10mTalk | Prompt-with-Me: in-IDE Structured Prompt Management for LLM-Driven Software Engineering Industry Showcase Ziyou Li Delft University of Technology, Agnia Sergeyuk JetBrains Research, Maliheh Izadi Delft University of Technology | ||
16:30 10mTalk | Are We SOLID Yet? An Empirical Study on Prompting LLMs to Detect Design Principle Violations NIER Track Fatih Pehlivan Bilkent University, Arçin Ülkü Ergüzen Bilkent University, Sahand Moslemi Yengejeh Bilkent University, Mayasah Lami Bilkent University, Anil Koyuncu Bilkent University | ||
16:40 10mTalk | Shrunk, Yet Complete: Code Shrinking-Resilient Android Third-Party Library Detection Industry Showcase Jingkun Zhang Institute of Software, Chinese Academy of Sciences; University of Chinese Academy of Sciences, Jingzheng Wu Institute of Software, The Chinese Academy of Sciences, Xiang Ling Institute of Software, Chinese Academy of Sciences, Tianyue Luo Institute of Software, Chinese Academy of Sciences, Bolin Zhou Institute of Software, Chinese Academy of Sciences; University of Chinese Academy of Sciences, Mutian Yang Beijing ZhongKeWeiLan Technology Co.,Ltd. | ||
16:50 10mTalk | LLM-Guided Genetic Improvement: Envisioning Semantic Aware Automated Software Evolution NIER Track Karine Even-Mendoza King’s College London, Alexander E.I. Brownlee University of Stirling, Alina Geiger Johannes Gutenberg University Mainz, Carol Hanna University College London, Justyna Petke University College London, Federica Sarro University College London, Dominik Sobania Johannes Gutenberg-Universität Mainz | ||