An empirical investigation of organic software product lines

Journal Article · 24 March 2021 · Empirical Software Engineering

DOI: https://doi.org/10.1007/s10664-021-09940-0 · OSTI ID:1772687

Cashman, Mikaela ; Firestone, Justin ; Cohen, Myra B. ; Thianniwet, Thammasak; Niu, Wei

Abstract Software product line engineering is a best practice for managing reuse in families of software systems that is increasingly being applied to novel and emerging domains. In this work we investigate the use of software product line engineering in one of these new domains, synthetic biology. In synthetic biology living organisms are programmed to perform new functions or improve existing functions. These programs are designed and constructed using small building blocks made out of DNA. We conjecture that there are families of products that consist of common and variable DNA parts, and we can leverage product line engineering to help synthetic biologists build, evolve, and reuse DNA parts. In this paper we perform an investigation of domain engineering that leverages an open-source repository of more than 45,000 reusable DNA parts. We show the feasibility of these new types of product line models by identifying features and related artifacts in up to 93.5% of products, and that there is indeed both commonality and variability. We then construct feature models for four commonly engineered functions leading to product lines ranging from 10 to 7.5 × 10 ²⁰ products. In a case study we demonstrate how we can use the feature models to help guide new experimentation in aspects of application engineering. Finally, in an empirical study we demonstrate the effectiveness and efficiency of automated reverse engineering on both complete and incomplete sets of products. In the process of these studies, we highlight key challenges and uncovered limitations of existing SPL techniques and tools which provide a roadmap for making SPL engineering applicable to new and emerging domains.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: National Inst. of Justice (NIJ); National Science Foundation (NSF); USDOE; USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1772687
• Journal Information: Empirical Software Engineering, Journal Name: Empirical Software Engineering Journal Issue: 3 Vol. 26; ISSN 1382-3256
• Publisher: Springer Science + Business MediaCopyright Statement
• Country of Publication: Netherlands
• Language: English