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plots and tables used for RQ1improved
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Simon Rüdiger Steuer committed Sep 28, 2023
1 parent 5382403 commit 7a283bd
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Showing 2 changed files with 179 additions and 195 deletions.
278 changes: 95 additions & 183 deletions varats/varats/plots/feature_blame_plots.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@
import pandas as pd
import seaborn as sns

from varats.data.metrics import apply_tukeys_fence
from varats.data.reports.feature_blame_report import (
StructuralFeatureBlameReport as SFBR,
)
Expand Down Expand Up @@ -91,110 +92,74 @@ def get_structural_commit_data_for_case_study(case_study: CaseStudy) -> pd.DataF
######## FEATURES #########


class FeatureSizeCorrSFBRPlot(Plot, plot_name="feature_size_corr_sfbr_plot"):
class FeatureSFBRPlot(Plot, plot_name="feature_sfbr_plot"):
def plot(self, view_mode: bool) -> None:
case_studies: tp.List[CaseStudy] = self.plot_kwargs["case_studies"]

df = pd.concat(
[
get_structural_feature_data_for_case_study(case_study)
for case_study in case_studies
]
)

plt = sns.regplot(data=df, x="feature_size", y="num_interacting_commits")

plt.set(xlabel="Feature Size", ylabel="Number Interacting Commits")


class FeatureSizeCorrSFBRPlotGenerator(
PlotGenerator,
generator_name="feature-size-corr-sfbr-plot",
options=[REQUIRE_MULTI_CASE_STUDY],
):
def generate(self) -> tp.List[Plot]:
case_studies: tp.List[CaseStudy] = self.plot_kwargs.pop("case_study")
return [
FeatureSizeCorrSFBRPlot(
self.plot_config, case_studies=case_studies, **self.plot_kwargs
fig, naxs = pyplot.subplots(len(case_studies), 3, figsize=(18, 18))
fig.suptitle("Structural Interactions of Features")
first: bool = True
for axs, case_study in zip(naxs, case_studies):
data = get_structural_feature_data_for_case_study(case_study)

data = data.sort_values(by=["num_interacting_commits_nd1"])
index = ["" for _ in range(len(data))]

stacked_feature_data = pd.DataFrame(
{
"Interacting with ND1": data["num_interacting_commits_nd1"].values,
"Interacting with ND>1": data[
"num_interacting_commits_nd>1"
].values,
},
index=index,
)
]

stacked_feature_data.plot.bar(stacked=True, width=0.95, ax=axs[0])
axs[0].set_xlabel("Features" if first else "", size="13")
axs[0].set_ylabel("Num Interacting Commits" if first else "", size="13")
axs[0].set_title(case_study.project_name, size="16")

class FeatureDisSFBRPlot(Plot, plot_name="feature_dis_sfbr_plot"):
def plot(self, view_mode: bool) -> None:
case_studies: tp.List[CaseStudy] = self.plot_kwargs["case_studies"]
dfs = [
get_structural_feature_data_for_case_study(case_study)
for case_study in case_studies
]
data = pd.concat(
[
df.assign(project=[case_study.project_name for _ in range(0, len(df))])
for case_study, df in zip(case_studies, dfs)
]
)
data = data.sort_values(by=["def_feature_size"])

plt = sns.boxplot(
data=data,
x="num_interacting_commits",
y="project",
)
ticks = 5
start = min(data["num_interacting_commits"])
stop = max(data["num_interacting_commits"])
step = round((stop - start) / ticks)
plt.set_xticks(range(start, stop + step, step), range(start, stop + step, step))
plt.set(
xlabel="Number Interacting Commits",
ylabel="Project",
title="Structural Interactions of Features",
)


class FeatureDisSFBRPlotGenerator(
PlotGenerator,
generator_name="feature-dis-sfbr-plot",
options=[REQUIRE_MULTI_CASE_STUDY],
):
def generate(self) -> tp.List[Plot]:
case_studies: tp.List[CaseStudy] = self.plot_kwargs.pop("case_study")
return [
FeatureDisSFBRPlot(
self.plot_config, case_studies=case_studies, **self.plot_kwargs
stacked_feature_size_data = pd.DataFrame(
{
"Definite Feature Size": data["def_feature_size"].values,
"Potential Feature Size": data["pot_feature_size"].values
- data["def_feature_size"].values,
},
index=index,
)
]


class FeatureSizeDisSFBRPlot(Plot, plot_name="feature_size_dis_sfbr_plot"):
def plot(self, view_mode: bool) -> None:
case_studies: tp.List[CaseStudy] = self.plot_kwargs["case_studies"]

df = pd.concat(
[
get_structural_feature_data_for_case_study(case_study)
for case_study in case_studies
]
)
stacked_feature_size_data.plot.bar(stacked=True, width=0.95, ax=axs[1])
axs[1].set_xlabel("")
axs[1].set_ylabel("Feature Size" if first else "", size="13")

df = df.sort_values(by=["feature_size"])
sns.regplot(
data=data,
x="def_feature_size",
y="num_interacting_commits_nd1",
ax=axs[2],
)
sns.regplot(
data=data, x="pot_feature_size", y="num_interacting_commits", ax=axs[2]
)

plt = sns.barplot(data=df, x="feature", y="feature_size", color="steelblue")
plt.set(xlabel="Feature", ylabel="Size", title="")
axs[2].set_xlabel("Feature Size" if first else "", size="13")
axs[2].set_ylabel("Num Interacting Commits" if first else "", size="13")

xticklabels = [str(i) for i in range(0, len(df))]
plt.set(xticklabels=xticklabels)
first = False


class FeatureSizeDisSFBRPlotGenerator(
class FeatureSFBRPlotGenerator(
PlotGenerator,
generator_name="feature-size-dis-sfbr-plot",
generator_name="feature-sfbr-plot",
options=[REQUIRE_MULTI_CASE_STUDY],
):
def generate(self) -> tp.List[Plot]:
case_studies: tp.List[CaseStudy] = self.plot_kwargs.pop("case_study")
return [
FeatureSizeDisSFBRPlot(
FeatureSFBRPlot(
self.plot_config, case_studies=case_studies, **self.plot_kwargs
)
]
Expand All @@ -203,125 +168,72 @@ def generate(self) -> tp.List[Plot]:
######## COMMITS #########


def get_pie_data_for_commit_data(commit_data) -> (tp.List[int], tp.List[int]):
min_num_interacting_features = min(commit_data)
max_num_interacting_features = max(commit_data)

data = [
0 for _ in range(min_num_interacting_features, max_num_interacting_features + 1)
]
add_s = lambda x: "" if x == 1 else "s"
labels = [
"Impl. " + str(i) + " feature" + add_s(i)
for i in range(min_num_interacting_features, max_num_interacting_features + 1)
]

for num_interacting_features in commit_data:
data[num_interacting_features - min_num_interacting_features] = (
data[num_interacting_features - min_num_interacting_features] + 1
)

adj_labels, adj_data = ([], [])
for i in range(0, max_num_interacting_features - min_num_interacting_features + 1):
if data[i] == 0:
continue
frac = data[i] / len(commit_data)
if frac < 0.05:
num_interacting_features = i + min_num_interacting_features
adj_labels.append(
"Impl. >="
+ str(num_interacting_features)
+ " feature"
+ add_s(num_interacting_features)
)
adj_data.append(np.sum(data[i:]))
break
adj_labels.append(labels[i])
adj_data.append(data[i])

return (adj_data, adj_labels)


class CommitSFBRPieChart(Plot, plot_name="commit_sfbr_pie_chart"):
def plot(self, view_mode: bool) -> None:
case_studies: tp.List[CaseStudy] = self.plot_kwargs["case_studies"]

def func(pct):
absolute = int(np.round(pct / 100.0 * len(commit_data)))
return f"{absolute:d}"

fig, naxs = pyplot.subplots(2, 2, figsize=(15, 15))
case_study_counter = 0
for axs in naxs:
for ax in axs:
case_study = case_studies[case_study_counter]
commit_data = get_structural_commit_data_for_case_study(case_study).loc[
:, "num_interacting_features"
]
data, labels = get_pie_data_for_commit_data(commit_data)
explode = [0.1] + [0 for _ in range(1, len(data))]
ax.pie(
data, labels=labels, explode=explode, autopct=lambda pct: func(pct)
)
ax.set_title(case_study.project_name)
case_study_counter += 1


class CommitSFBRPieChartGenerator(
PlotGenerator,
generator_name="commit-sfbr-pie-chart",
options=[REQUIRE_MULTI_CASE_STUDY],
):
def generate(self) -> tp.List[Plot]:
case_studies: tp.List[CaseStudy] = self.plot_kwargs.pop("case_study")
return [
CommitSFBRPieChart(
self.plot_config, case_studies=case_studies, **self.plot_kwargs
)
]


class CommitSpecificSFBRPlot(Plot, plot_name="commit_specific_sfbr_plot"):
def plot(self, view_mode: bool) -> None:
case_studies: tp.List[CaseStudy] = self.plot_kwargs["case_studies"]

fig, naxs = pyplot.subplots(2, 2, figsize=(15, 15))
case_study_counter = 0
for axs in naxs:
for nth_ax in axs:
for ax in axs:
if case_study_counter == len(case_studies):
continue
case_study = case_studies[case_study_counter]

commit_data = get_structural_commit_data_for_case_study(case_study)
commit_data = commit_data.sort_values(by=["num_interacting_features"])[
"num_interacting_features"
]
commit_data = commit_data.sort_values(by=["num_interacting_features"])

filter_lrg_commits = apply_tukeys_fence(
commit_data, column="commit_size", k=1.5
)

commit_data = commit_data["num_interacting_features"]

interacting_with_nd1 = [
commit_data[index][0] for index in commit_data.index
commit_data[index][0] if index in filter_lrg_commits.index else 0
for index in commit_data.index
]
interacting_with_at_leat_nd2 = [
sum(commit_data[index][1:]) for index in commit_data.index
interacting_with_at_least_nd2 = [
sum(commit_data[index][1:])
if index in filter_lrg_commits.index
else 0
for index in commit_data.index
]
interacting_with_nd1_lrg_commit = [
0 if index in filter_lrg_commits.index else commit_data[index][0]
for index in commit_data.index
]
interacting_with_at_least_nd2_lrg_commit = [
0
if index in filter_lrg_commits.index
else sum(commit_data[index][1:])
for index in commit_data.index
]
stacked_commit_data = pd.DataFrame(
{
"Min Nesting Degree 1": interacting_with_nd1,
"Min Nesting Degree >=2": interacting_with_at_leat_nd2,
},
index=commit_data.index,
)

stacked_commit_data.plot.bar(stacked=True, width=0.95, ax=nth_ax)

nth_ax.set_xlabel("Commits")
nth_ax.set_ylabel("Num Interacting Features")
rng = range(len(commit_data))
ax.bar(rng, interacting_with_nd1)
ax.bar(
rng,
interacting_with_at_least_nd2,
bottom=interacting_with_nd1,
)
ax.bar(rng, interacting_with_nd1_lrg_commit, alpha=0.65, color="tab:blue")
ax.bar(
rng,
interacting_with_at_least_nd2_lrg_commit,
bottom=interacting_with_nd1_lrg_commit,
alpha=0.65,
color="tab:orange",
)
ax.set_xlabel("Commits")
ax.set_ylabel("Num Interacting Features")
step = round(len(commit_data) / 6)
nth_ax.set_xticks(
ax.set_xticks(
ticks=[i * step for i in range(6)],
labels=[str(i * step) for i in range(6)],
)
nth_ax.set_title(case_study.project_name)
ax.set_title(case_study.project_name)
ax.legend(["Interacting with ND1", "Interacting with ND>1", "ND1, Large Commit", "ND>1, Large Commit"])
case_study_counter += 1


Expand Down Expand Up @@ -822,7 +734,7 @@ def plot(self, view_mode: bool) -> None:
else:
ax.set_xlabel("")
ax.set_ylabel("")
x_rng = range(0, len(author_data), 2)
x_rng = range(1, len(author_data) + 1, 2)
ax.set_xticks(ticks=x_rng, labels=[str(i) for i in x_rng])
max_impl_authors = max(author_data["num_implementing_authors"])
y_rng = range(1, max_impl_authors + 1)
Expand Down
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