God Components in Apache Tika

How did big, bulky software components come into being? In this project, we explore the evolution of so-called God Components; a software anti-pattern where pieces of software with a large number of classes or lines of code get so large over time they become hard to maintain and reason about.

The codebase of choice is chosen to be Apache Tika, a content analysis toolkit built in Java. This Jupyter Notebook provides a structured analysis on data mined using Designite and using data from the Git repository. A report on the git contributers can be found here (made using gitinspector).

→ By Jeroen Overschie and Konstantina Gkikopouli.

Loading datasets

Import dependencies.

import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

Load commit data.

all_commits = pd.read_csv('output/all_commits.csv', parse_dates=['datetime'], index_col='commit')
all_commits

	author	email	datetime	message	jira
commit
5ad435d39e39558b98af3af5aca291eef22651f0	tallison	tallison@apache.org	2021-01-12 15:10:23-05:00	TIKA-3267 change boolean getXYZ to boolean isXYZ	TIKA-3267
e8b4305ce80df59e45352e73ceb972f4534c984c	tallison	tallison@apache.org	2021-01-11 17:05:01-05:00	TIKA-3269 update artifacts for 2.0.0	TIKA-3269
b0fb00ef6a11db121c56436b9542a4cf248fef8b	Tim Allison	tallison@apache.org	2021-01-11 15:48:10-05:00	TIKA-3266 (#396)	TIKA-3266
566c5962101db0be4ff56c6ff3740c05701a3c0e	THausherr	tilman@snafu.de	2021-01-10 14:21:20+01:00	TIKA-3244: update zstd	TIKA-3244
d732cc17ea555572d1b1fe9e041931fb36567cdf	tallison	tallison@apache.org	2021-01-07 16:45:31-05:00	TIKA-3268 -- throw exception if excluded parse...	TIKA-3268
...	...	...	...	...	...
c5417bed9a7f84acb913f0197e241ecb0d1205b4	Jukka Zitting	jukka@apache.org	2007-03-31 12:40:53+00:00	TIKA-2: Basic web site based on Maven 2.	TIKA-2
6e750bb72e8be60c27bcf6bf6dd3a05742c812fc	Jukka Zitting	jukka@apache.org	2007-03-31 10:35:09+00:00	TIKA-4: Ignore Eclipse project files.	TIKA-4
3794e9a8a99172f9666befbba686af29276200ea	Jukka Zitting	jukka@apache.org	2007-03-31 10:31:15+00:00	TIKA-4: Basic Maven 2 POM and source tree for ...	TIKA-4
f10cd4207c54fc45e4b244b5e538f9943c697c5b	Jukka Zitting	jukka@apache.org	2007-03-31 07:40:09+00:00	TIKA-1: Standard README, NOTICE, and LICENSE f...	TIKA-1
19ab44f873feae6d8acedfe662d1839e4aece854	Jukka Zitting	jukka@apache.org	2007-03-31 07:22:55+00:00	TIKA-1: Standard {trunk,branches,tags} setup	TIKA-1

4984 rows × 5 columns

Note that some commit authors are actually the same person, but under different aliases. Let's fix this using a simple mapping.

len(all_commits['author'].unique())

127

authormap = pd.read_csv('output/authormap.csv')
all_commits['author'] = all_commits['author']\
    .replace(authormap['from'].values, authormap['to'].values)
len(all_commits['author'].unique())

111

Dispose some columns we don't need.

all_commits = all_commits[['author', 'datetime', 'jira']]

Make sure the date rows are indeed parsed as a datetime.

all_commits['datetime'].values[0]

datetime.datetime(2021, 1, 12, 15, 10, 23, tzinfo=tzoffset(None, -18000))

Load the aggregated report file, all_reports.csv. Contains Designite report data for every single Tika commit.

all_reports = pd.read_csv('output/all_reports.csv', dtype={'package': 'category'},
    index_col=['package', 'commit'])
all_reports = all_reports.rename(columns={'metric': '# classes'}) # rename col
all_reports

		repo	smell	cause	# classes
package	commit
org.apache.tika.example	49bb4691393c016d8d65e6b11febca9e56feedef	tika-cpu_21	God Component	MANY_CLASSES	49
org.apache.tika.batch	49bb4691393c016d8d65e6b11febca9e56feedef	tika-cpu_21	God Component	MANY_CLASSES	31
org.apache.tika.detect	49bb4691393c016d8d65e6b11febca9e56feedef	tika-cpu_21	God Component	MANY_CLASSES	31
org.apache.tika.parser	49bb4691393c016d8d65e6b11febca9e56feedef	tika-cpu_21	God Component	MANY_CLASSES	37
org.apache.tika.mime	49bb4691393c016d8d65e6b11febca9e56feedef	tika-cpu_21	God Component	MANY_CLASSES	31
...	...	...	...	...	...
org.apache.tika.sax	77d57d5506cfacf4e75d09e214643deda5a52047	tika-cpu_17	God Component	MANY_CLASSES	35
org.apache.tika.parser.txt	77d57d5506cfacf4e75d09e214643deda5a52047	tika-cpu_17	God Component	MANY_CLASSES	74
org.apache.tika.sax	edb6775bf356eaaf656730589cc3340a15b602ea	tika-cpu_21	God Component	MANY_CLASSES	33
org.apache.tika.parser.txt	edb6775bf356eaaf656730589cc3340a15b602ea	tika-cpu_21	God Component	MANY_CLASSES	71
org.apache.tika.parser.microsoft	edb6775bf356eaaf656730589cc3340a15b602ea	tika-cpu_21	God Component	MANY_CLASSES	31

28077 rows × 4 columns

Dispose some columns.

all_reports = all_reports[['# classes']]

Add commits to report data, combining them into one big dataset, gcdata.

gcdata = all_reports.join(all_commits)

God component lifetime

General statistics on lifetime.

# Compute date data
dt = gcdata.groupby('package')['datetime']
normalize_date = lambda date: pd.to_datetime(date, utc=True)\
                    .dt.tz_convert('Europe/Amsterdam')
dtmin = normalize_date(dt.min())
dtmax = normalize_date(dt.max())
became_gc   = dtmin.dt.strftime('%Y-%m-%d')
last_gc     = dtmax.dt.strftime('%Y-%m-%d')
gc_commits  = dt.count()
gc_days     = (dtmax - dtmin).dt.days

# Compute author data
authors = gcdata.groupby('package')['author'].unique()
n_authors = authors.transform(lambda x: len(x))

# DataFrame
stats = pd.DataFrame([became_gc, last_gc, gc_commits, gc_days, n_authors],
        ['Became GC at', 'Last seen as GC', '# GC commits', '# GC days', '# authors'])\
        .transpose().reset_index()
stats

	package	Became GC at	Last seen as GC	# GC commits	# GC days	# authors
0	org.apache.tika.batch	2015-06-28	2020-12-14	2352	1996	100
1	org.apache.tika.detect	2017-01-19	2020-12-14	1579	1424	71
2	org.apache.tika.example	2015-05-04	2020-12-14	2433	2050	101
3	org.apache.tika.fork	2018-05-31	2020-12-14	738	927	45
4	org.apache.tika.metadata	2016-09-26	2020-12-14	1743	1539	77
5	org.apache.tika.mime	2015-05-02	2020-12-14	2443	2053	101
6	org.apache.tika.parser	2015-02-21	2020-12-14	2429	2123	102
7	org.apache.tika.parser.microsoft	2011-11-25	2020-12-14	3052	3306	105
8	org.apache.tika.parser.microsoft.chm	2020-08-21	2020-12-14	155	114	11
9	org.apache.tika.parser.microsoft.onenote	2019-12-16	2020-12-14	305	363	22
10	org.apache.tika.parser.microsoft.ooxml	2017-03-23	2020-12-14	1454	1361	69
11	org.apache.tika.parser.txt	2009-05-22	2020-12-14	4539	4223	107
12	org.apache.tika.sax	2011-09-25	2020-12-14	3691	3367	105
13	org.apache.tika.server	2014-05-07	2020-12-14	1078	2412	38
14	org.apache.tika.utils	2020-10-31	2020-12-14	86	44	7

'Average GC lifetime: ' + str(gc_days.mean() / 365) + ' years'

'Average GC lifetime: 4.986666666666667 years'

Average over the above stats

stats.agg({
    '# GC commits': 'mean',
    '# GC days': 'mean',
    '# authors': 'mean'
})

# GC commits    1871.800000
# GC days       1820.133333
# authors         70.733333
dtype: float64

Total amount of God Components

total_gcs = gcdata.groupby(['commit', 'datetime']).count().reset_index()
sns.lineplot(data=total_gcs, x='datetime', y='# classes')
plt.ylabel('# god components')

Text(0, 0.5, '# god components')

Amount of classes per God Component

fig, ax = plt.subplots(figsize=(10, 4.2))
g = sns.lineplot(data=gcdata.sort_values('package'),
    x='datetime', y='# classes', hue='package', ax=ax)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)

<matplotlib.legend.Legend at 0x1244e21f0>

Computing the # classes chronological difference (delta)

gcdelta = all_reports.groupby('package')\
    .apply(lambda df: df.merge(all_commits, on='commit', how='right'))
gcdelta['# classes diff'] = gcdelta['# classes'].diff(periods=-1).fillna(0).astype(int)
gcdelta['is gc'] = gcdelta['# classes'] > 0
gcdelta['is gc diff'] = gcdelta['is gc'].diff(periods=-1).fillna(False)
gcdelta['anchor'] = 0
gcdelta['# classes added']   = gcdelta[['anchor', '# classes diff']].max(axis=1)
gcdelta['# classes removed'] = gcdelta[['anchor', '# classes diff']].min(axis=1)
gcdelta = gcdelta.drop(columns=['anchor'])
print('gcdelta has {} total rows'.format(len(gcdelta)))
gcdelta[gcdelta['# classes diff'] > 0]

gcdelta has 74760 total rows

		# classes	author	datetime	jira	# classes diff	is gc	is gc diff	# classes added	# classes removed
package	commit
org.apache.tika.batch	30c3d8104a51f015416382995435a4785059f07c	32.0	Tim Allison	2018-11-13 14:08:26-05:00	TIKA-2778	1	True	False	1	0
org.apache.tika.detect	8b82b4c942c82f9cc2eb393e669227606b5f15fc	37.0	Tim Allison	2020-12-04 13:16:28-05:00	TIKA-3218	1	True	False	1	0
	a43784b19f6b0955478dded71521b0491d21c90b	36.0	Tim Allison	2020-12-02 11:58:44-05:00	TIKA-3241	1	True	False	1	0
	ee8caf69456bd52d278283792dc1b9c56477c243	36.0	Tim Allison	2020-10-29 10:57:03-04:00	TIKA-3215	4	True	False	4	0
	70ca280f11fe4127df290b8027c6bc1d5180271f	32.0	Nassif	2017-09-08 12:36:48-03:00	TIKA-2460	1	True	False	1	0
...	...	...	...	...	...	...	...	...	...	...
org.apache.tika.server	f776fc07aecdf4e97343cba7ee7d35d1cd9743df	39.0	Tim Allison	2014-12-19 03:12:38+00:00	TIKA-1497	1	True	False	1	0
	57db5ef21c95f49aa19b9da69a69d27a2a1da5ad	38.0	Tim Allison	2014-12-18 19:50:52+00:00	TIKA-1498	4	True	False	4	0
	95051f2a352f3e7ee83acf203d75b803a26c3f4f	34.0	Sergey Beryozkin	2014-08-06 08:06:52+00:00	TIKA-1371	1	True	False	1	0
	583af266a28f6494c29fcd62fa78fcc32199cc2e	33.0	Chris Mattmann	2014-06-14 18:52:44+00:00	TIKA-1336	2	True	False	2	0
org.apache.tika.utils	8b82b4c942c82f9cc2eb393e669227606b5f15fc	32.0	Tim Allison	2020-12-04 13:16:28-05:00	TIKA-3218	1	True	False	1	0

144 rows × 9 columns

import matplotlib.dates as md
fig, ax = plt.subplots(figsize=(10, 4.2))
# scatter `_` symbols such that it looks like a continuous line.
sns.scatterplot(data=gcdelta[gcdelta['is gc']],\
    x='datetime', y='package', hue='package',\
    ax=ax, legend=False, marker='_', edgecolor=None)
# start- and ending markers for GC lifetime.
sns.scatterplot(data=gcdelta[gcdelta['is gc diff']],\
    x='datetime', y='package', hue='package',\
    ax=ax, edgecolor=None, markers={False: '.', True: '>'},\
    style='is gc', alpha=0.75)
ax.xaxis.set_major_locator(md.YearLocator())
ax.xaxis.set_major_formatter(md.DateFormatter('%Y'))
ax.axes.yaxis.set_visible(False)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)

<matplotlib.legend.Legend at 0x1246b34f0>

Save a small version of is gc metric the # classes dataframe only where abstract difference > 3

gcdelta[gcdelta['# classes diff'].abs() > 3]\
    .sort_values(['package', 'datetime'], ascending=True)\
    .to_csv('output/diffs_nclasses.csv',  
        columns=['# classes', '# classes diff', '# classes added', '# classes removed', 'author', 'datetime', 'jira'],
        float_format='%.f')
gcdelta[gcdelta['is gc diff']]\
    .sort_values(['package', 'datetime'], ascending=True)\
    .to_csv('output/diffs_isgc.csv',  
        columns=['is gc', '# classes', '# classes diff', 'author', 'datetime', 'jira'],
        float_format='%.f')

God Component growth in terms of Lines Of Code (LOC)

Load up data on Lines Of Code for every God Component at the state of every commit.

all_locs = pd.read_csv('output/all_locs.csv', index_col=['package', 'commit'])
all_locs

		additions	deletions	LOC	change
package	commit
org.apache.tika.batch	fe4cd58cced0e15f1848afbc2518ab4a66e6867f	6638	0	6638	6638
	dbc5eb76b7a21587eba83724e36bff1cb29e72c6	251	227	6662	24
	8054dddc156444a0e67a66fcd2dbc9b4bb5db4b7	13	14	6661	-1
	4ae33b70378e0ee66b7d9c95d6fd7d51b10cc658	28	26	6663	2
	6a013f550bf7e8cfbed4652ec3785e1627d0f7ea	353	334	6682	19
...	...	...	...	...	...
org.apache.tika.utils	813a6eb69853d87ae54fc4bf6183267bb480322d	2	2	4969	0
	7fd2825613bce5fd741a0126de8d812a316389ba	2	2	4969	0
	dd85c73094dc87f6f6e208278325be810761f490	1	1	4969	0
	326b7d7abda238ccc181fc2acaf2271e08fe9c1b	1	1	4969	0
	7f65d61b4fe1f4c1d4929bbe1c456c68afd44b6c	1	1	4969	0

4505 rows × 4 columns

Add commit datetime.

locdata = all_locs.join(all_commits)

fig, ax = plt.subplots(figsize=(10, 4.2))
g = sns.lineplot(data=locdata, x='datetime', y='LOC', hue='package', ax=ax)
g.set(yscale='log')
g.yaxis.set_major_formatter(ticker.ScalarFormatter())
plt.ylabel('Lines Of Code (LOC)')
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)

<matplotlib.legend.Legend at 0x1247d2640>

Investigating what developers contribute to GC buildup

How many developers contributed to God Components? We aim to answer the question in terms of both God Component (1) buildup and (2) refactoring. We do this, by considering the # classes added and # classes removed for each developer.

gcdelta['# classes changed'] = gcdelta['# classes diff'].abs()
df = gcdelta[gcdelta['# classes changed'] > 0]\
    .groupby('author')\
    .agg('sum')[['# classes changed', '# classes added', '# classes removed']]
totalclasses = df['# classes changed'].sum()
df['% contributed'] = df['# classes changed'] / totalclasses * 100
df.sort_values('% contributed', ascending=False)

	# classes changed	# classes added	# classes removed	% contributed
author
Tim Allison	314	191	-123	46.587537
Nick Burch	85	42	-43	12.611276
Thamme Gowda	60	54	-6	8.902077
Lewis John McGibbney	49	23	-26	7.270030
Giuseppe Totaro	30	28	-2	4.451039
Chris Mattmann	23	8	-15	3.412463
amensiko	18	11	-7	2.670623
ThejanW	11	0	-11	1.632047
Cameron Rollheiser	11	0	-11	1.632047
Jukka Zitting	9	7	-2	1.335312
Nassif	9	2	-7	1.335312
Sergey Beryozkin	9	9	0	1.335312
Matthew Caruana Galizia	8	0	-8	1.186944
Manali Shah	6	2	-4	0.890208
Tom Barber	5	3	-2	0.741840
Konstantin Gribov	5	4	-1	0.741840
Bob Paulin	3	0	-3	0.445104
avtar singh	3	3	0	0.445104
Tyler Palsulich	3	3	0	0.445104
nandan-pc	3	0	-3	0.445104
John Patrick	3	3	0	0.445104
Maxim Valyanskiy	2	2	0	0.296736
Nicholas DiPiazza	1	1	0	0.148368
Ray Gauss II	1	1	0	0.148368
PeterAlfredLee	1	1	0	0.148368
Kenneth William Krugler	1	1	0	0.148368
smadha	1	0	-1	0.148368

topn = df.nlargest(8, '% contributed')

# Compute a row that sums 'All others' except for the top n authors.
allothers = df.nsmallest(len(df) - len(topn), '% contributed')
lastn = allothers.sum().to_dict()
lastn['author'] = 'All others ({})'.format(len(allothers.index.unique()))
least = pd.DataFrame([lastn]).set_index('author')
classescontrib = pd.concat([topn, least])\
    .sort_values('% contributed', ascending=False)
# Pie chart
g = classescontrib.plot.pie(y='% contributed', figsize=(7, 7), colormap='Dark2')
plt.ylabel('')
plt.title('% contributed in # God Component classes changed')
plt.legend(loc="upper left", bbox_to_anchor=(1,1))

<matplotlib.legend.Legend at 0x1247d0370>

topnames = topn.index.unique()
# Top contributers sum of # classes changed
topcontr = gcdelta['author'].isin(topnames)
nettotop = gcdelta[topcontr].reset_index()\
    .groupby('author').sum()[['# classes added', '# classes removed']]
# All others sum of # classes changed
others = ~gcdelta['author'].isin(topnames)
lastn = gcdelta[others].reset_index()\
    .groupby('author').sum()[['# classes added', '# classes removed']]\
    .sum().to_dict()
lastn['author'] = 'All others'
least = pd.DataFrame([lastn]).set_index('author')

pd.concat([nettotop, least])\
    .plot.bar(stacked=True, figsize=(10, 5))
plt.xticks(rotation=45)
plt.ylabel('# classes added / removed')
plt.title('Top contributers versus # classes added / removed')

Text(0.5, 1.0, 'Top contributers versus # classes added / removed')

Also, show that there are not a lot of developers working on the entire project at all. Developers versus LOC's added/removed:

df = locdata.groupby('author').agg('sum').drop(columns=['LOC'])
totalchange = df['change'].sum()
df['% contributed'] = df['change'] / totalchange * 100
df.sort_values('% contributed', ascending=False)

	additions	deletions	change	% contributed
author
Tim Allison	212484	143092	69392	28.026980
Chris Mattmann	47236	13721	33515	13.536492
Jukka Zitting	77686	47854	29832	12.048952
Nick Burch	42384	12628	29756	12.018256
Madhav Sharan	27264	3846	23418	9.458379
...	...	...	...	...
Hasan Kara	0	1	-1	-0.000404
Ioannis Kakavas	5	6	-1	-0.000404
Javen O'Neal	26	36	-10	-0.004039
Diffblue assistant	15	68	-53	-0.021406
Yash Tanna	48	121	-73	-0.029484

102 rows × 4 columns

topn = df.nlargest(7, '% contributed')

# Compute a row that sums 'All others' except for the top n authors.
lastn = df.nsmallest(len(df) - len(topn), '% contributed').sum().to_dict()
lastn['author'] = 'All others'
least = pd.DataFrame([lastn]).set_index('author')
pd.concat([topn, least])\
    .plot.pie(y='% contributed', figsize=(7, 7))
plt.ylabel('')
plt.title('% contributed in # Lines Of Code changed')
plt.legend(loc="upper left", bbox_to_anchor=(1,1))

<matplotlib.legend.Legend at 0x124a43d60>

Jira Issues analysis

Add the Tika Jira issue tracker information as a data source.

all_issues = pd.read_csv('output/all_issues.csv', dtype={'issuetype': 'category'},
    index_col='jira')
all_issues = all_issues.drop(columns=['id', 'self', 'reporter', 'updated'])
all_issues

	resolution	priority	assignee	status	creator	issuetype	resolutiondate	created	components
jira
TIKA-3256	Fixed	Minor	tilman	Resolved	tilman	Task	2020-12-27T13:08:45.000+0000	2020-12-27T13:07:15.000+0000	general
TIKA-3255	NaN	Major	NaN	Open	peterkronenberg	Bug	NaN	2020-12-22T17:04:55.000+0000	NaN
TIKA-3254	NaN	Major	NaN	Open	sathia	Bug	NaN	2020-12-22T13:49:17.000+0000	NaN
TIKA-3253	NaN	Minor	NaN	Open	tilman	Improvement	NaN	2020-12-17T08:57:18.000+0000	tika-eval
TIKA-3252	NaN	Trivial	NaN	Open	tilman	Bug	NaN	2020-12-17T08:38:45.000+0000	documentation
...	...	...	...	...	...	...	...	...	...
TIKA-5	Fixed	Major	chrismattmann	Closed	chrismattmann	New Feature	2007-06-08T22:55:39.000+0000	2007-05-31T21:49:14.000+0000	general
TIKA-4	Fixed	Major	jukkaz	Closed	jukkaz	Task	2007-03-31T13:48:32.000+0000	2007-03-31T06:58:59.000+0000	NaN
TIKA-3	Fixed	Major	jukkaz	Closed	jukkaz	Task	2007-03-31T09:33:24.000+0000	2007-03-31T06:57:02.000+0000	NaN
TIKA-2	Fixed	Major	jukkaz	Closed	jukkaz	Task	2007-03-31T13:49:41.000+0000	2007-03-31T06:55:41.000+0000	documentation
TIKA-1	Fixed	Major	jukkaz	Closed	jukkaz	Task	2007-03-31T07:43:18.000+0000	2007-03-31T06:54:09.000+0000	general

3248 rows × 9 columns

Keep only issue key and issue type columns.

all_issues = all_issues[['issuetype']]

Generally, how many and what types of issues are in Apache Tika's Jira issue tracker?

print(all_issues.value_counts().sum(), 'issues total.')
all_issues.value_counts()

3248 issues total.

issuetype  
Bug            1592
Improvement    1056
Task            260
New Feature     219
Sub-task         65
Wish             46
Test             10
dtype: int64

... of which these amounts are involved in God Component commits:

issuedata = gcdata.reset_index().merge(all_issues, on='jira') # combine with GC data
issuetypes = issuedata[['jira', 'issuetype']].drop_duplicates()[['issuetype']]
print(issuetypes.value_counts().sum(), 'issues total related to GC commits.')
issuetypes.value_counts()

1694 issues total related to GC commits.

issuetype  
Bug            708
Improvement    643
Task           178
New Feature    107
Sub-task        37
Wish            17
Test             4
dtype: int64

... which is this percentage:

issuetypes.value_counts() / all_issues.value_counts()

issuetype  
Bug            0.444724
Improvement    0.608902
Task           0.684615
New Feature    0.488584
Sub-task       0.569231
Wish           0.369565
Test           0.400000
dtype: float64

We can also check what issue types are represented most in the God Component commits:

issuetypes.value_counts(normalize=True)

issuetype  
Bug            0.417946
Improvement    0.379575
Task           0.105077
New Feature    0.063164
Sub-task       0.021842
Wish           0.010035
Test           0.002361
dtype: float64

Build a pivot table and show heatmap.

issuedata = gcdata.reset_index().merge(all_issues, on='jira') # combine with GC data
typecounts = issuedata.set_index(['package', 'issuetype']).index.value_counts()
typecountdf = pd.DataFrame({ 'count': typecounts.values }, 
    index=pd.MultiIndex.from_tuples(typecounts.keys(), names=['package', 'issuetype']))\
        .reset_index()
typecountdf

	package	issuetype	count
0	org.apache.tika.parser.txt	Improvement	1252
1	org.apache.tika.parser.txt	Bug	1164
2	org.apache.tika.sax	Improvement	961
3	org.apache.tika.sax	Bug	906
4	org.apache.tika.parser.microsoft	Improvement	721
...	...	...	...
85	org.apache.tika.server	Test	3
86	org.apache.tika.parser.microsoft.chm	New Feature	2
87	org.apache.tika.parser	Test	2
88	org.apache.tika.fork	Sub-task	1
89	org.apache.tika.utils	New Feature	1

90 rows × 3 columns

rectangular = typecountdf.pivot(index='package', columns='issuetype', values='count')
f, ax = plt.subplots()
plt.yticks(rotation=0)
plt.xticks(rotation=45)
g = sns.heatmap(rectangular,\
    annot=True, fmt='.0f', linewidths=.5,\
    cmap="YlGnBu", yticklabels=True, ax=ax)
plt.title('Amount of commits related to issue types per GC')

Text(0.5, 1.0, 'Amount of commits related to issue types per GC')

Issue types that contribute to GC buildup

This time around, only include those commits that actually 'build up' or 'decrease' the size of a God Component; i.e. those commits that actually: add or remove classes to a GC.

gd = gcdelta.reset_index().merge(all_issues, on='jira')
netto = gd.groupby('issuetype').sum()[['# classes added', '# classes removed']]
netto

	# classes added	# classes removed
issuetype
Bug	48	-51
Improvement	143	-102
New Feature	24	-16
Sub-task	4	-4
Task	56	-39
Test	0	0
Wish	0	0

netto.plot.bar(stacked=True, colormap='Paired')
plt.xticks(rotation=45)
plt.ylabel('# classes')

Text(0, 0.5, '# classes')