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Generation of New Scalarizing Functions Using Genetic Programming Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Human-Like Summaries from Heterogeneous and Time-Windowed Software Development Artefacts

Authors : Mahfouth Alghamdi, Christoph Treude, Markus Wagner

Published in: Parallel Problem Solving from Nature – PPSN XVI

Publisher: Springer International Publishing

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Abstract

Automatic text summarisation has drawn considerable interest in the area of software engineering. It is challenging to summarise the activities related to a software project, (1) because of the volume and heterogeneity of involved software artefacts, and (2) because it is unclear what information a developer seeks in such a multi-document summary. We present the first framework for summarising multi-document software artefacts containing heterogeneous data within a given time frame. To produce human-like summaries, we employ a range of iterative heuristics to minimise the cosine-similarity between texts and high-dimensional feature vectors. A first study shows that users find the automatically generated summaries the most useful when they are generated using word similarity and based on the eight most relevant software artefacts.

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Footnotes
1
https://​github.​com/​nodejs/​node, accessed on February 2, 2020.
 
2
Based on a two-sided Mann-Whitney U test, there is no statistically significant difference at p = 0.05 between Greedy and Brute Force.
 
3
Let us recall let Random Selection does not generate only one summary at random, but many until the time limit is reached, and it then returns the best.
 
4
The average running time per algorithm (in seconds) to generate a summary is, from left: 151.92, 0.49, 10.0, 10.0, 10.20, 6.67 s.
 
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Metadata
Title
Human-Like Summaries from Heterogeneous and Time-Windowed Software Development Artefacts
Authors
Mahfouth Alghamdi
Christoph Treude
Markus Wagner
Copyright Year
2020
Book
Parallel Problem Solving from Nature – PPSN XVI

Print ISBN: 978-3-030-58114-5

Electronic ISBN: 978-3-030-58115-2

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-58115-2_23

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