CS2125 Paper Review Form - Winter 2019

Reviewer: Yilin Han    

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)
 [ ] Good 
 [X] 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)
 [ ] Marginal depth
 [X] Little or no depth

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

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

6) Overall Rating
 [ ] Strong accept (award quality)
 [ ] Accept (high quality - would argue for acceptance)
 [ ] Weak Accept (borderline, but lean towards acceptance)
 [X] 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)

This paper focuses on the challenges that people will face while developing a machine learning product. It researched seven projects, and the authors summarize from these projects and get twelve main challenges. These challengs covers not only deep learning, but also covers the intersection between Dl and other technologies, such as big data, software engineering and techinical debt. 

I would not recommend this paper, since I believe anyone who have little background is able to understand these difficulties. Since they are coming from the limitation of machine learning itself. I would say this paper only have little benefit for people whoe are new to ML or about to manage a ML project. 

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

S1: None

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

W1:The tecinical depth is very low for this paper.