CS2125 Paper Review Form - Winter 2019

Reviewer: Zi Yi Chen

Paper Title: Software Engineering Challenges of Deep Learning

Author(s): Anders Arpteg, Björn Brinne, Luka Crnkovic-Friis, Jan Bosch

1) Is the paper technically correct?
 [X] Yes
 [ ] Mostly (minor flaws, but mostly solid)
 [ ] No

2) Originality
 [ ] Very good (very novel, trailblazing work)
 [X] Good 
 [ ] Marginal (very incremental)
 [ ] Poor (little or nothing that is new)

3) Technical Depth
 [ ] Very good (comparable to best conference papers)
 [ ] Good (comparable to typical conference papers)
 [X] Marginal depth
 [ ] Little or no depth

4) Impact/Significance
 [ ] Very significant
 [X] Significant
 [ ] Marginal significance.
 [ ] Little or no significance.

5) Presentation
 [X] Very well written
 [ ] Generally well written
 [ ] Readable
 [ ] Needs considerable work
 [ ] Unacceptably bad

6) Overall Rating
 [ ] Strong accept (award quality)
 [X] Accept (high quality - would argue for acceptance)
 [ ] Weak Accept (borderline, but lean towards acceptance)
 [ ] Weak Reject (not sure why this paper was published)


7) Summary of the paper's main contribution and rationale
   for your recommendation. (1-2 paragraphs)

In this paper, the authorrs described seven different real world machine learning projects and mapped them to relevant software engineering challenges. The authors went in depth into what effects each of those challenges can have in real world projects, namely the consequences and limitations of applying machine learning algorithms. 

The paper was easy to read and follow. Although the technical depth of this paper is on the lower side, but the mentioned challenges are crucial to the success implementing machine learning algorithms in real world projects. 


8) List 1-3 strengths of the paper.  (1-2 sentences each,
identified as S1, S2, S3.)

S1: This paper was well organized. Contents have a good flow from background, projects, challenges, mappings, to conclusion.
S2: The challenges were well explained with plenty of examples.


9) List 1-3 weaknesses of the paper (1-2 sentences each,
identified as W1, W2, W3.)

W1: There were little technical depth in this paper. It could use less challenge examples and add couple technical examples.