In [1]:
%run prelude.ipy
Table of Contents
The between programs are intended to test the effects of pulled-out versus inline functionality.
We expected that having functionality pulled out would benefit experienced programmers, who would more quickly chunk the definitions and interpret the main body of the program. Less experienced programmer were expected to take more time and refer to function definitions more often.
Code¶
between_functions.py
def between(numbers, low, high):
winners = []
for num in numbers:
if (low < num) and (num < high):
winners.append(num)
return winners
def common(list1, list2):
winners = []
for item1 in list1:
if item1 in list2:
winners.append(item1)
return winners
x = [2, 8, 7, 9, -5, 0, 2]
x_btwn = between(x, 2, 10)
print x_btwn
y = [1, -3, 10, 0, 8, 9, 1]
y_btwn = between(y, -2, 9)
print y_btwn
xy_common = common(x, y)
print xy_common
between_inline.py
x = [2, 8, 7, 9, -5, 0, 2]
x_between = []
for x_i in x:
if (2 < x_i) and (x_i < 10):
x_between.append(x_i)
print x_between
y = [1, -3, 10, 0, 8, 9, 1]
y_between = []
for y_i in y:
if (-2 < y_i) and (y_i < 9):
y_between.append(y_i)
print y_between
xy_common = []
for x_i in x:
if x_i in y:
xy_common.append(x_i)
print xy_common
Output
[8, 7, 9]
[1, 0, 8, 1]
[8, 9, 0]
Metrics
- Lines of code: 24
- Cyclomatic complexity: 7
- Halstead effort: 94192
- Halstead volume: 830
Output
[8, 7, 9]
[1, 0, 8, 1]
[8, 9, 0]
Metrics
- Lines of code: 19
- Cyclomatic complexity: 7
- Halstead effort: 45596
- Halstead volume: 661
Response Data¶
In [2]:
between_responses = util.filter_program(responses, "between")
functions_responses = util.filter_program(responses, "between", "functions")
inline_responses = util.filter_program(responses, "between", "inline")
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axes = plot.misc.grade_distributions(between_responses)
axes[0].figure
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axes = plot.misc.duration_distributions(between_responses)
axes[0].figure
Out[13]:
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print "both"
print between_responses[["duration", "grade_value"]].corr(method="spearman").values[0, 1]
print ""
for version, frame in between_responses.groupby("version"):
print version
print frame[["duration", "grade_value"]].corr(method="spearman").values[0, 1]
print ""
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axes = plot.misc.python_experience_distributions(between_responses)
axes[0].figure
Out[15]:
In [16]:
axes = plot.misc.programming_experience_distributions(between_responses)
axes[0].figure
Out[16]:
In []:
print "both"
print between_responses[["duration", "grade_value"]].corr(method="spearman").values[0, 1]
print ""
for version, frame in between_responses.groupby("version"):
print version
print frame[["duration", "grade_value"]].corr(method="spearman").values[0, 1]
print ""
In [113]:
results = eye_analysis.classify.feature_importances(between_responses,
["prog_years", "py_years", "degree_num", "age", "gender_num", "duration"], "grade_common",
num_estimators=250)
ax = plot.misc.feature_importances(results)
ax.set_title("Feature Importances")
ax.figure.tight_layout()
ax.figure
Out[113]:
In [73]:
results = eye_analysis.classify.area_under_curve(between_responses, ["prog_years", "py_years", "degree_num", "age", "gender_num", "duration"], "grade_common")
fig = pyplot.figure()
ax = pyplot.axes()
results.boxplot(by="classifier", ax=ax)
fig
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In [7]:
a, b = util.split_by_boolean(functions_responses, "grade_correct")
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pyplot.figure()
a.duration.hist().figure
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pyplot.figure()
b.duration.hist().figure
Out[11]:
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a.duration.median(), b.duration.median()
Out[14]:
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pyplot.figure()
a.py_years.hist().figure
Out[19]:
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pyplot.figure()
b.py_years.hist().figure
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Eye-Tracking¶
In [3]:
functions_fixations = util.filter_program(line_fixations, "between", "functions")
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img = plot.fixations.line_code_image(functions_fixations, code_image("between", "functions"))
img.thumbnail((800,800), Image.ANTIALIAS)
img
Out[21]:
In [25]:
ax = plot.fixations.line_transitions(functions_fixations, num_lines=24, figsize=(20, 10), show_probs=False)
ax.set_title("Line Transitions (between functions)")
ax.figure
Out[25]:
In [73]:
ax = plot.fixations.line_timeline(functions_fixations, step_size=200)
ax.figure.tight_layout()
ax.figure
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ax = plot.fixations.line_timeline_steps(functions_fixations)
ax.figure.tight_layout()
ax.figure
Out[5]:
In [154]:
metrics.num_fixations(functions_fixations)
Out[154]:
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metrics.fixation_duration_per_aoi(functions_fixations, text_lengths)
Out[131]:
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metrics.time_to_first_fixation(functions_fixations, "line 10")
Out[146]:
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metrics.percent_fixations_on_aoi(functions_fixations, "line 10", 10)
Out[152]:
In [153]:
metrics.average_fixation_duration(functions_fixations)
Out[153]:
In [60]:
import matplotlib.cm as cm
In [69]:
first_trial = functions_fixations.trial_id.values[0]
trial_fixations = functions_fixations[functions_fixations.trial_id == first_trial]
density, counts = metrics.fixation_spatial_density(trial_fixations,
grid_bbox=(0, 0, 1920, 1080), num_rows=20, num_cols=20, threshold=1)
print density
screen_image = data.hansen_2012.trial_screen(first_trial)
img = plot.fixations.spatial_density_heatmap(counts, screen_image, cmap=cm.coolwarm)
img
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masked_counts = np.ma.masked_where(counts < 1, counts)
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masked_counts
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trial_groups = functions_fixations.groupby("trial_id")
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visited, ids = metrics.lines_by_time(functions_fixations, step_size=1000)
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visited.shape
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functions_fixations
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