development emails content analyzer: intention mining in developer discussions
TRANSCRIPT
![Page 1: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/1.jpg)
Development Emails Content Analyzer: Intention Mining in Developer Discussions
Andrea Di Sorbo
Sebastiano Panichella
Corrado Visaggio
Massimiliano Di Penta
Gerardo Canfora
Harald Gall
![Page 2: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/2.jpg)
Outline
Context: Wri5en Development Discussions Case Study: Development Mailing List of 2 Open Source Projects
Results: Automatic Classification of Relevant Contents in Developers’ Communication
2
![Page 3: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/3.jpg)
Open Source (OS) and Industrial Projects
3
![Page 4: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/4.jpg)
Open Source (OS) and Industrial Projects
4
![Page 5: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/5.jpg)
Open Source (OS) and Industrial Projects
5
![Page 6: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/6.jpg)
Open Source (OS) and Industrial Projects
6
![Page 7: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/7.jpg)
Development Communication Means
Recommender systems: -‐‑ Bug Triaging [1] -‐‑ Suggest Mentors [2] -‐‑ Code re-‐‑documentation [3] -‐‑ Etc.
[1] Anvik et al. “Who should fix this bug?”. [2] Canfora et al. “Who is going to mentor newcomers in open source projects?” [3] Panichella et al. “Mining source code descriptions from developer communications” 7
![Page 8: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/8.jpg)
Development Communication Means
8
![Page 9: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/9.jpg)
Development Communication Means
[1] Bacchelli et al. “Content classification of development emails”. [2] Cerulo et al. “A Hidden Markov Model to detect coded information islands in free text.”
9
![Page 10: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/10.jpg)
Different Kinds of Data
Structured
Semi-‐‑Structured
Unstructured
10
![Page 11: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/11.jpg)
A Considerable Effort for Developers
Many messages
Developers get lost in unnecessary details missing potential useful information…
11
![Page 12: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/12.jpg)
Previous Work
12
Hana et al.
“…Lazy” RTC occurs when a core developer post a change to a mailing lists and nobody responds, it assumed that other developers reviewed the
code…”
![Page 13: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/13.jpg)
Previous Work
Approaches for: -‐‑ Generating summaries of emails. à Lam et al. , à Rambow et al.
-‐‑ Generating summaries of bug reports. à Rastkar et al.
13
![Page 14: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/14.jpg)
Different Purposes
Feature requests
Bug disclosures
Project Management
14
![Page 15: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/15.jpg)
DECA (Development Email Content Analyzer)
An approach to Classify Paragraphs According to Intentions
hSp://www.ifi.uzh.ch/seal/people/panichella/tools/DECA.html 15
![Page 16: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/16.jpg)
Why use NLP for Classifying Paragraphs According to
Intentions?
16
![Page 17: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/17.jpg)
Example
i. We could use a leaky bucket algorithm to limit the bandwidth
ii. The leaky bucket algorithm fails in limiting the bandwidth
17
![Page 18: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/18.jpg)
i. We could use a leaky bucket algorithm to limit the bandwidth
ii. The leaky bucket algorithm fails in limiting the bandwidth
An high percentage of words in common
Example
18
![Page 19: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/19.jpg)
i. We could use a leaky bucket algorithm to limit the bandwidth
ii. The leaky bucket algorithm fails in limiting the bandwidth
Discuss about the same topics
Example
19
![Page 20: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/20.jpg)
i. We could use a leaky bucket algorithm to limit the bandwidth
ii. The leaky bucket algorithm fails in limiting the bandwidth
Have different intentions
Example
20
![Page 21: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/21.jpg)
i. We could use a leaky bucket algorithm to limit the bandwidth
ii. The leaky bucket algorithm fails in limiting the bandwidth
Have different intentions
Example
“Techniques based on lexicon analysis, such as VSM [1], LSI [2], or LDA [3] would not be sufficient to classify paragraphs according to intentions”. .
[1] Baeza-‐‑Yates et al. “Modern Information Retrieval”. [2] de Marneffe et al., “The Stanford typed dependencies representation”. [3] Blei et al., “Latent dirichlet allocation”.
21
![Page 22: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/22.jpg)
Perspective
22
![Page 23: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/23.jpg)
Goal: Understanding to what extent NL parsing could be used in recognizing informative text fragments in emails from a software maintenance and evolution perspective Quality focus: Detection of text paragraphs in development discussions containing helpful information for developers. Perspective: Guide developers in maintaining and evolving their products.
Case Study
23
![Page 24: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/24.jpg)
Research Questions
RQ1: Can an NLP approach (i.e. DECA) be effective in classifying writers’ intentions in development emails?
RQ2: Is DECA more effective than existing Machine Learning techniques in classifying development emails content?
24
![Page 25: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/25.jpg)
Qt
Ubuntu
Context
25
![Page 26: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/26.jpg)
STEPS: 1) Taxonomy Definition
2) Classification Based on DECA (NLP Analyzer)
26
![Page 27: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/27.jpg)
Taxonomy Definition
27
![Page 28: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/28.jpg)
Sampling We selected 100
Of the Project
28
![Page 29: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/29.jpg)
Clustering
Clusters Implementation
Technical Infrastructure Project Status
Social Interations Usage
Discarded
Guzzi et. al – MSR2013
29
![Page 30: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/30.jpg)
Clustering
Guzzi et. al – ICSE2012
30
![Page 31: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/31.jpg)
The final taxonomy
31
![Page 32: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/32.jpg)
Differences with Guzzi et. al.
32
![Page 33: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/33.jpg)
Examples
33
![Page 34: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/34.jpg)
Natural Language Parsing
DECA (Development Email Content Analyzer)
34
![Page 35: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/35.jpg)
Recurrent Linguistic PaSerns
35
![Page 36: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/36.jpg)
Why NL parsing? Well defined predicate-‐‑argument structures
use
we could algorithm
a leaky bucket
limit
to bandwidth
the
nsubj aux dobj xcomp
det amod nn aux dobj
det
fails
algorithm
the leaky bucket
in
limiting
bandwidth
the
nsubj prep
det amod nn pcomp
dobj
det
36
![Page 37: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/37.jpg)
NL parsing Natural Language Templates
use
[someone] could [something]
nsubj aux dobj
fails
[somehing]
nsubj
37
![Page 38: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/38.jpg)
Natural Language Templates
use
[someone] could [something]
nsubj aux dobj
fails
[somehing]
nsubj
NL parsing
38
![Page 39: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/39.jpg)
Natural Language Templates
use
[someone] could [something]
nsubj aux dobj
fails
[somehing]
nsubj
NL parsing
39
![Page 40: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/40.jpg)
NLP Heuristics
40
![Page 41: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/41.jpg)
NLP Parser
raw text NLP parser NLP heuristics
41
![Page 42: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/42.jpg)
42
![Page 43: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/43.jpg)
43
![Page 44: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/44.jpg)
RQ1: Is DECA effective in
classifying writers’ intentions in development emails?
44
![Page 45: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/45.jpg)
Experiment I
training
test
102 87
100
45
![Page 46: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/46.jpg)
Experiment I
training
test
102 87
100
Experiment II False
Negative 46
![Page 47: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/47.jpg)
Experiment II
training 100 169
test 100
Experiment III False
Negative 47
![Page 48: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/48.jpg)
Experiment III
training 100 231
test 100
48
![Page 49: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/49.jpg)
49
![Page 50: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/50.jpg)
50
![Page 51: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/51.jpg)
51
![Page 52: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/52.jpg)
52
![Page 53: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/53.jpg)
53
![Page 54: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/54.jpg)
54
![Page 55: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/55.jpg)
RQ2: Is the proposed approach more
effective than existing ML in classifying development emails content?
55
![Page 56: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/56.jpg)
ML for Email Classification
An Approach Based on ML for Email Content Classification
à Antoniol et. al., CASCON 2008 à Zhou et al. , ICSME 2014
56
![Page 57: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/57.jpg)
ML for Email Classification
An Approach Based on ML for Email Content Classification 1)Text Features
57
![Page 58: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/58.jpg)
ML for Email Classification
An Approach Based on ML for Email Content Classification 1)Text Features
2) Split training and test sets
58
![Page 59: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/59.jpg)
ML for Email Classification
An Approach Based on ML for Email Content Classification 1)Text Features
2) Split training and test sets
3) Oracle building
59
![Page 60: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/60.jpg)
ML for Email Classification
An Approach Based on ML for Email Content Classification 1)Text Features
2) Split training and test sets
3) Oracle building
4) Classification
training
prediction
à Antoniol et. al., CASCON 2008 à Zhou et al. , ICSME 2014
60
![Page 61: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/61.jpg)
61
![Page 62: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/62.jpg)
62
![Page 63: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/63.jpg)
63
![Page 64: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/64.jpg)
64
![Page 65: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/65.jpg)
65
![Page 66: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/66.jpg)
66
![Page 67: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/67.jpg)
67
![Page 68: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/68.jpg)
68
![Page 69: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/69.jpg)
69
![Page 70: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/70.jpg)
Summary
• RQ2: DECA outperforms traditional ML techniques in terms of recall, precision and F-Measure when classifying e-mail content.
• RQ1: the automatic classification performed by DECA achieves very good results in terms of both precision, recall and F-measure (over all the experiments).
70
![Page 71: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/71.jpg)
Summary
• RQ2: DECA outperforms traditional ML techniques in terms of recall, precision and F-Measure when classifying e-mail content.
”…it took the MSR community more than 10 years to figure out that machine learning is not the best method for analyzing human-written text. Thank you for helping move the field forward…” [One of the ASE Reviewers]
• RQ1: the automatic classification performed by DECA achieves very good results in terms of both precision, recall and F-measure (over all the experiments).
71
![Page 72: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/72.jpg)
72
![Page 73: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/73.jpg)
Code e-‐‑documentation
àPanichella et. al. – ICPC 2012 Extract methods’ descriptions from developers discussions
à Vector Space Models à ad hoc heuristics
“… several are the discourse paIerns that characterize false negative method descriptions… “
73
![Page 74: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/74.jpg)
Code re-‐‑documentation “… several are the discourse
paIerns that characterize false negative method descriptions… “
74
![Page 75: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/75.jpg)
Code re-‐‑documentation “… several are the discourse
paIerns that characterize false negative method descriptions… “
75
![Page 76: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/76.jpg)
Code re-‐‑documentation “… several are the discourse
paIerns that characterize false negative method descriptions… “
76
![Page 77: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/77.jpg)
Code re-‐‑documentation “… several are the discourse
paIerns that characterize false negative method descriptions… “
77
![Page 78: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/78.jpg)
Code re-‐‑documentation “… several are the discourse
paIerns that characterize false negative method descriptions… “
78
![Page 79: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/79.jpg)
Code re-‐‑documentation “… several are the discourse
paIerns that characterize false negative method descriptions… “
79
![Page 80: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/80.jpg)
Code re-‐‑documentation
delete
80
![Page 81: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/81.jpg)
Conclusion
81
![Page 82: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/82.jpg)
Conclusion
82
![Page 83: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/83.jpg)
Conclusion
83
![Page 84: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/84.jpg)
Conclusion
84
![Page 85: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/85.jpg)
Conclusion
85
![Page 86: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/86.jpg)
Conclusion
86
![Page 87: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/87.jpg)
Future work
1)DECA as preprocessing support to discard irrelevant sentences in summarization
approaches
87
![Page 88: Development Emails Content Analyzer: Intention Mining in Developer Discussions](https://reader031.vdocuments.site/reader031/viewer/2022030307/58e937131a28ab84768b4d99/html5/thumbnails/88.jpg)
Future work
1)DECA as preprocessing support to discard irrelevant sentences in summarization
approaches
2)DECA in combination with topic models for mining
contents with the same intentions and the same topics
88