TY - GEN
T1 - Analysis of Software Engineering Practices in General Software and Machine Learning Startups
AU - Lakha, Bishal
AU - Bhetwal, Kalyan
AU - Eisty, Nasir U.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Context: On top of the inherent challenges startup software companies face applying proper software engineering practices, the non-deterministic nature of machine learning techniques makes it even more difficult for machine learning (ML) startups. Objective: Therefore, the objective of our study is to understand the whole picture of software engineering practices followed by ML startups and identify additional needs. Method: To achieve our goal, we conducted a systematic literature review study on 37 papers published in the last 21 years. We selected papers on both general software startups and ML startups. We collected data to understand software engineering (SE) practices in five phases of the software development life-cycle: requirement engineering, design, development, quality assurance, and deployment. Results: We find some interesting differences in software engineering practices in ML startups and general software startups. The data management and model learning phases are the most prominent among them. Conclusion: While ML startups face many similar challenges to general software startups, the additional difficulties of using stochastic ML models require different strategies in using software engineering practices to produce high-quality products.
AB - Context: On top of the inherent challenges startup software companies face applying proper software engineering practices, the non-deterministic nature of machine learning techniques makes it even more difficult for machine learning (ML) startups. Objective: Therefore, the objective of our study is to understand the whole picture of software engineering practices followed by ML startups and identify additional needs. Method: To achieve our goal, we conducted a systematic literature review study on 37 papers published in the last 21 years. We selected papers on both general software startups and ML startups. We collected data to understand software engineering (SE) practices in five phases of the software development life-cycle: requirement engineering, design, development, quality assurance, and deployment. Results: We find some interesting differences in software engineering practices in ML startups and general software startups. The data management and model learning phases are the most prominent among them. Conclusion: While ML startups face many similar challenges to general software startups, the additional difficulties of using stochastic ML models require different strategies in using software engineering practices to produce high-quality products.
KW - Machine Learning Startups
KW - Software Engineering
KW - Software Startups
KW - Systematic Literature Review
UR - http://www.scopus.com/inward/record.url?scp=85166283059&partnerID=8YFLogxK
U2 - 10.1109/SERA57763.2023.10197836
DO - 10.1109/SERA57763.2023.10197836
M3 - Conference contribution
AN - SCOPUS:85166283059
T3 - Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023
SP - 39
EP - 46
BT - Proceedings - 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications, SERA 2023
A2 - Song, Yeong-Tae
A2 - Rhee, Junghwan
A2 - Jeon, Yuseok
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE/ACIS International Conference on Software Engineering Research, Management and Applications, SERA 2023
Y2 - 23 May 2023 through 25 May 2023
ER -