Steps for Trend Analysis

1. Data Preparation
   • Ensure that your data is cleaned and formatted (we already handled this step).
   • Aggregate the data to the time periods that you are interested in (e.g., daily, weekly, monthly).
2. Statistical Analysis
   • Descriptive Statistics: To get a sense of the central tendency (mean, median), variability (standard deviation), and range of performance metrics.
   • Time Series Analysis: To detect trends over time in execution time, memory usage, and error counts.
   • Correlation Analysis: To check if certain performance metrics are related (e.g., does higher memory usage correlate with longer execution time?).
   • Moving Averages: To smooth out short-term fluctuations and highlight longer-term trends.
3. Visualizations
   • Line Plots: To visualize trends over time.
   • Histograms: To show the distribution of values (e.g., execution times).
   • Scatter Plots: To visualize relationships between variables.
   • Heatmaps: To visualize correlations between different performance metrics.

Python Code for Trend Analysis

Here’s a detailed example of how to carry out trend analysis in Python using libraries like pandas, matplotlib, seaborn, and statsmodels.

1. Prepare Data for Time Series Analysis

First, we’ll aggregate the data into daily, weekly, or monthly periods, depending on the trend we want to analyze. Let’s assume we are interested in monthly trends.

pythonCopy# Set 'timestamp' as the index for time-based aggregation
df_combined.set_index('timestamp', inplace=True)

# Resample the data by month and calculate mean for each KPI
monthly_data = df_combined.resample('M').agg({
    'Execution Time (ms)': 'mean',    # Average execution time per month
    'CPU Usage (%)': 'mean',          # Average CPU usage per month
    'Memory (MB)': 'mean',            # Average memory usage per month
    'Error Count': 'sum'              # Total number of errors per month
})

# Preview the resampled data
print(monthly_data.head())

2. Descriptive Statistics

Let’s calculate some basic statistics to get a sense of how the program has been performing across time.

pythonCopy# Descriptive statistics (mean, std, min, max) for each column
print(monthly_data.describe())

This will output the following (for example):

• Mean execution time per month.
• Standard deviation of CPU usage over months.
• Min/Max values for error counts.

3. Time Series Trend Visualization

Next, let’s visualize how each KPI has evolved over time (monthly in this case). We’ll use line plots to track the trends.

pythonCopyimport matplotlib.pyplot as plt

# Plot Execution Time over Time (Monthly)
plt.figure(figsize=(10, 6))
plt.plot(monthly_data.index, monthly_data['Execution Time (ms)'], label='Execution Time (ms)', color='blue')
plt.xlabel('Month')
plt.ylabel('Execution Time (ms)')
plt.title('Monthly Execution Time Trend')
plt.xticks(rotation=45)
plt.legend()
plt.grid(True)
plt.show()

# Plot CPU Usage over Time (Monthly)
plt.figure(figsize=(10, 6))
plt.plot(monthly_data.index, monthly_data['CPU Usage (%)'], label='CPU Usage (%)', color='red')
plt.xlabel('Month')
plt.ylabel('CPU Usage (%)')
plt.title('Monthly CPU Usage Trend')
plt.xticks(rotation=45)
plt.legend()
plt.grid(True)
plt.show()

# Plot Memory Usage over Time (Monthly)
plt.figure(figsize=(10, 6))
plt.plot(monthly_data.index, monthly_data['Memory (MB)'], label='Memory Usage (MB)', color='green')
plt.xlabel('Month')
plt.ylabel('Memory Usage (MB)')
plt.title('Monthly Memory Usage Trend')
plt.xticks(rotation=45)
plt.legend()
plt.grid(True)
plt.show()

# Plot Error Counts over Time (Monthly)
plt.figure(figsize=(10, 6))
plt.plot(monthly_data.index, monthly_data['Error Count'], label='Error Count', color='purple')
plt.xlabel('Month')
plt.ylabel('Error Count')
plt.title('Monthly Error Count Trend')
plt.xticks(rotation=45)
plt.legend()
plt.grid(True)
plt.show()

4. Trendline or Moving Average

To better visualize underlying trends, you can apply a moving average. This helps to smooth out short-term fluctuations and highlight long-term trends.

pythonCopy# Apply moving average (e.g., 3-month window)
monthly_data['Execution Time (ms) MA'] = monthly_data['Execution Time (ms)'].rolling(window=3).mean()

# Plot with moving average for Execution Time
plt.figure(figsize=(10, 6))
plt.plot(monthly_data.index, monthly_data['Execution Time (ms)'], label='Execution Time (ms)', color='blue')
plt.plot(monthly_data.index, monthly_data['Execution Time (ms) MA'], label='3-Month Moving Average', color='orange', linestyle='--')
plt.xlabel('Month')
plt.ylabel('Execution Time (ms)')
plt.title('Execution Time Trend with Moving Average')
plt.xticks(rotation=45)
plt.legend()
plt.grid(True)
plt.show()

5. Correlation Analysis

You may want to investigate whether certain performance metrics are correlated. For example, does CPU usage correlate with execution time? To do this, you can calculate the correlation matrix.

pythonCopy# Calculate correlation between the different performance metrics
correlation_matrix = monthly_data[['Execution Time (ms)', 'CPU Usage (%)', 'Memory (MB)', 'Error Count']].corr()

# Display the correlation matrix
import seaborn as sns

plt.figure(figsize=(8, 6))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)
plt.title('Correlation Matrix of Performance Metrics')
plt.show()

This will show you how closely related the different metrics are (e.g., if higher CPU usage correlates with higher execution times or if error counts are related to resource usage).

6. Statistical Significance (Optional)

You may also want to check if the observed trends are statistically significant. For this, you can use linear regression to model the trends and check if there is a statistically significant slope.

pythonCopyimport statsmodels.api as sm

# Convert 'timestamp' to ordinal values for regression
monthly_data['timestamp_ordinal'] = monthly_data.index.map(lambda x: x.toordinal())

# Perform regression on Execution Time vs. Time
X = sm.add_constant(monthly_data['timestamp_ordinal'])  # Add constant for intercept
y = monthly_data['Execution Time (ms)']

# Fit the model
model = sm.OLS(y, X).fit()

# Display the regression summary
print(model.summary())

The regression output will give you:

• Coefficients (e.g., slope and intercept) that describe the trend.
• P-value to assess the statistical significance of the trend.

Key Insights to Look For in Trend Analysis:

1. Overall Trends: Look for increasing or decreasing trends in execution time, memory usage, and error counts. An increasing execution time over months may suggest that performance is degrading.
2. Anomalies: Detect any anomalies or spikes in performance metrics, such as a sudden increase in error count or CPU usage, which might require investigation.
3. Seasonality: Identify if there are any seasonal trends, such as performance degradation during certain months or periods of heavy usage.
4. Correlation: Check if any metrics are strongly correlated. For example, high memory usage might correlate with high CPU usage and longer execution times.
5. Moving Average: The moving average will help smooth out the noise and make underlying trends more visible.