DSDGen: Extracting Documentation to Comprehend Fork Merges

Jacob Krüger; Alex Mikulinski; Sandro Schulze; Thomas Leich; Gunter Saake

doi:10.1145/3579028.3609015

DSDGen: Extracting Documentation to Comprehend Fork Merges

Jacob Krüger, Alex Mikulinski, Sandro Schulze, Thomas Leich, Gunter Saake

Engineering of Software-Intensive Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

3 Citations (Scopus)

15 Downloads (Pure)

Abstract

Developers use the forking mechanisms of modern social-coding platforms to evolve and maintain their systems. Using such mechanisms often leads to a larger number of independent variants with individual features or bug fixes that the developers may want to merge after a longer period of co-evolution. At this point, they may have forgotten (or never had) knowledge about differences between the variants. Tackling this problem, we built on the idea of on-demand documentation to develop a technique that automatically extracts and presents information for merging a class from two forks. We implemented our technique as a prototype called DSDGen and evaluated it through an experimental simulation with 10 students who should comprehend two real -world merge requests. Using DSDGen instead of code diffs only, more of the students could correctly comprehend the merges (6 / 10 versus 2 / 10) within a similar time. The students actively inspected the additional information provided by DSDGen and used it to comprehend the differences between the forked classes. So, DSDGen can help developers recover information for comprehending the differences caused by fork co-evolution during merges, with our results indicating opportunities for future research and improvements.

Original language	English
Title of host publication	SPLC '23
Subtitle of host publication	Proceedings of the 27th ACM International Systems and Software Product Line Conference - Volume B
Editors	Paolo Arcaini, Maurice H. ter Beek, Gilles Perrouin, Iris Reinhartz-Berger, Ivan Machado, Silvia Regina Vergilio, Rick Rabiser, Tao Yue, Xavier Devroey, Mónica Pinto, Hironori Washizaki
Publisher	Association for Computing Machinery, Inc
Pages	47-56
Number of pages	10
ISBN (Electronic)	979-8-4007-0092-7
DOIs	https://doi.org/10.1145/3579028.3609015
Publication status	Published - 28 Aug 2023
Event	27th ACM International Systems and Software Product Line Conference, SPLC 2023 - Tokyo, Japan Duration: 28 Aug 2023 → 1 Sept 2023

Conference

Conference	27th ACM International Systems and Software Product Line Conference, SPLC 2023
Country/Territory	Japan
City	Tokyo
Period	28/08/23 → 1/09/23

Funding

The research reported in this paper has been partially supported by the German Research Foundation (DFG) project EXPLANT (LE 3382/2-3, SA 465/49-3) [34].

Funders	Funder number
Deutsche Forschungsgemeinschaft	LE 3382/2-3, SA 465/49-3

Keywords

Program comprehension
Software documentation
Merging
Fork ecosystem
Software evolution
Variant-rich systems
program comprehension
merging
fork ecosystems
variant-rich systems
software documentation
software evolution

Access to Document

10.1145/3579028.3609015

pdfFinal published version, 958 KBLicence: CC BY

https://jacobkrueger.github.io/assets/papers/Krueger2023DSDGen.pdf

Cite this

Krüger, J., Mikulinski, A., Schulze, S., Leich, T., & Saake, G. (2023). DSDGen: Extracting Documentation to Comprehend Fork Merges. In P. Arcaini, M. H. ter Beek, G. Perrouin, I. Reinhartz-Berger, I. Machado, S. R. Vergilio, R. Rabiser, T. Yue, X. Devroey, M. Pinto, & H. Washizaki (Eds.), SPLC '23: Proceedings of the 27th ACM International Systems and Software Product Line Conference - Volume B (pp. 47-56). Association for Computing Machinery, Inc. https://doi.org/10.1145/3579028.3609015

Krüger, Jacob ; Mikulinski, Alex ; Schulze, Sandro et al. / DSDGen : Extracting Documentation to Comprehend Fork Merges. SPLC '23: Proceedings of the 27th ACM International Systems and Software Product Line Conference - Volume B. editor / Paolo Arcaini ; Maurice H. ter Beek ; Gilles Perrouin ; Iris Reinhartz-Berger ; Ivan Machado ; Silvia Regina Vergilio ; Rick Rabiser ; Tao Yue ; Xavier Devroey ; Mónica Pinto ; Hironori Washizaki. Association for Computing Machinery, Inc, 2023. pp. 47-56

@inproceedings{a11448882ca448e4a4060079cb751ec4,

title = "DSDGen: Extracting Documentation to Comprehend Fork Merges",

abstract = "Developers use the forking mechanisms of modern social-coding platforms to evolve and maintain their systems. Using such mechanisms often leads to a larger number of independent variants with individual features or bug fixes that the developers may want to merge after a longer period of co-evolution. At this point, they may have forgotten (or never had) knowledge about differences between the variants. Tackling this problem, we built on the idea of on-demand documentation to develop a technique that automatically extracts and presents information for merging a class from two forks. We implemented our technique as a prototype called DSDGen and evaluated it through an experimental simulation with 10 students who should comprehend two real -world merge requests. Using DSDGen instead of code diffs only, more of the students could correctly comprehend the merges (6 / 10 versus 2 / 10) within a similar time. The students actively inspected the additional information provided by DSDGen and used it to comprehend the differences between the forked classes. So, DSDGen can help developers recover information for comprehending the differences caused by fork co-evolution during merges, with our results indicating opportunities for future research and improvements.",

keywords = "Program comprehension, Software documentation, Merging, Fork ecosystem, Software evolution, Variant-rich systems, program comprehension, merging, fork ecosystems, variant-rich systems, software documentation, software evolution",

author = "Jacob Kr{\"u}ger and Alex Mikulinski and Sandro Schulze and Thomas Leich and Gunter Saake",

year = "2023",

month = aug,

day = "28",

doi = "10.1145/3579028.3609015",

language = "English",

pages = "47--56",

editor = "Paolo Arcaini and {ter Beek}, {Maurice H.} and Gilles Perrouin and Iris Reinhartz-Berger and Ivan Machado and Vergilio, {Silvia Regina} and Rick Rabiser and Tao Yue and Xavier Devroey and M{\'o}nica Pinto and Hironori Washizaki",

booktitle = "SPLC '23",

publisher = "Association for Computing Machinery, Inc",

address = "United States",

note = "27th ACM International Systems and Software Product Line Conference, SPLC 2023 ; Conference date: 28-08-2023 Through 01-09-2023",

}

Krüger, J, Mikulinski, A, Schulze, S, Leich, T & Saake, G 2023, DSDGen: Extracting Documentation to Comprehend Fork Merges. in P Arcaini, MH ter Beek, G Perrouin, I Reinhartz-Berger, I Machado, SR Vergilio, R Rabiser, T Yue, X Devroey, M Pinto & H Washizaki (eds), SPLC '23: Proceedings of the 27th ACM International Systems and Software Product Line Conference - Volume B. Association for Computing Machinery, Inc, pp. 47-56, 27th ACM International Systems and Software Product Line Conference, SPLC 2023, Tokyo, Japan, 28/08/23. https://doi.org/10.1145/3579028.3609015

DSDGen: Extracting Documentation to Comprehend Fork Merges. / Krüger, Jacob; Mikulinski, Alex; Schulze, Sandro et al.
SPLC '23: Proceedings of the 27th ACM International Systems and Software Product Line Conference - Volume B. ed. / Paolo Arcaini; Maurice H. ter Beek; Gilles Perrouin; Iris Reinhartz-Berger; Ivan Machado; Silvia Regina Vergilio; Rick Rabiser; Tao Yue; Xavier Devroey; Mónica Pinto; Hironori Washizaki. Association for Computing Machinery, Inc, 2023. p. 47-56.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - DSDGen

T2 - 27th ACM International Systems and Software Product Line Conference, SPLC 2023

AU - Krüger, Jacob

AU - Mikulinski, Alex

AU - Schulze, Sandro

AU - Leich, Thomas

AU - Saake, Gunter

PY - 2023/8/28

Y1 - 2023/8/28

N2 - Developers use the forking mechanisms of modern social-coding platforms to evolve and maintain their systems. Using such mechanisms often leads to a larger number of independent variants with individual features or bug fixes that the developers may want to merge after a longer period of co-evolution. At this point, they may have forgotten (or never had) knowledge about differences between the variants. Tackling this problem, we built on the idea of on-demand documentation to develop a technique that automatically extracts and presents information for merging a class from two forks. We implemented our technique as a prototype called DSDGen and evaluated it through an experimental simulation with 10 students who should comprehend two real -world merge requests. Using DSDGen instead of code diffs only, more of the students could correctly comprehend the merges (6 / 10 versus 2 / 10) within a similar time. The students actively inspected the additional information provided by DSDGen and used it to comprehend the differences between the forked classes. So, DSDGen can help developers recover information for comprehending the differences caused by fork co-evolution during merges, with our results indicating opportunities for future research and improvements.

AB - Developers use the forking mechanisms of modern social-coding platforms to evolve and maintain their systems. Using such mechanisms often leads to a larger number of independent variants with individual features or bug fixes that the developers may want to merge after a longer period of co-evolution. At this point, they may have forgotten (or never had) knowledge about differences between the variants. Tackling this problem, we built on the idea of on-demand documentation to develop a technique that automatically extracts and presents information for merging a class from two forks. We implemented our technique as a prototype called DSDGen and evaluated it through an experimental simulation with 10 students who should comprehend two real -world merge requests. Using DSDGen instead of code diffs only, more of the students could correctly comprehend the merges (6 / 10 versus 2 / 10) within a similar time. The students actively inspected the additional information provided by DSDGen and used it to comprehend the differences between the forked classes. So, DSDGen can help developers recover information for comprehending the differences caused by fork co-evolution during merges, with our results indicating opportunities for future research and improvements.

KW - Program comprehension

KW - Software documentation

KW - Merging

KW - Fork ecosystem

KW - Software evolution

KW - Variant-rich systems

KW - program comprehension

KW - merging

KW - fork ecosystems

KW - variant-rich systems

KW - software documentation

KW - software evolution

UR - http://www.scopus.com/inward/record.url?scp=85176006640&partnerID=8YFLogxK

U2 - 10.1145/3579028.3609015

DO - 10.1145/3579028.3609015

M3 - Conference contribution

SP - 47

EP - 56

BT - SPLC '23

A2 - Arcaini, Paolo

A2 - ter Beek, Maurice H.

A2 - Perrouin, Gilles

A2 - Reinhartz-Berger, Iris

A2 - Machado, Ivan

A2 - Vergilio, Silvia Regina

A2 - Rabiser, Rick

A2 - Yue, Tao

A2 - Devroey, Xavier

A2 - Pinto, Mónica

A2 - Washizaki, Hironori

PB - Association for Computing Machinery, Inc

Y2 - 28 August 2023 through 1 September 2023

ER -

Krüger J, Mikulinski A, Schulze S, Leich T, Saake G. DSDGen: Extracting Documentation to Comprehend Fork Merges. In Arcaini P, ter Beek MH, Perrouin G, Reinhartz-Berger I, Machado I, Vergilio SR, Rabiser R, Yue T, Devroey X, Pinto M, Washizaki H, editors, SPLC '23: Proceedings of the 27th ACM International Systems and Software Product Line Conference - Volume B. Association for Computing Machinery, Inc. 2023. p. 47-56 doi: 10.1145/3579028.3609015

DSDGen: Extracting Documentation to Comprehend Fork Merges

Abstract

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this