Commit-Based Activity Metrics

• Commit-Based Activity Metrics are quantitative measures that analyze time intervals between commits, providing clear insights into developer pace and project health.
• They employ statistical models like EPDF and ECDF along with aggregation methods to distinguish active projects from stagnant ones and to assess commit stability.
• Advanced techniques classify commit intents, filter automated commits, and detect social dynamics such as activity cascades, enhancing maintenance profiling and risk evaluation.

Commit-based activity metrics comprise a set of quantitative measures derived from the frequency, regularity, structure, and social dynamics of source code commits in collaborative software projects. As the commit represents the atomic unit of contribution in most modern version control systems, analysis of commit activity provides a foundational lens through which software development processes, project resilience, maintenance profiles, and developer behavior are understood. Commit-based metrics form the basis for models of project health, productivity, risk evaluation, and even community sustainability, serving as core instruments for empirical research in software engineering.

1. Conceptual Foundations and Statistical Formulation

The defining variable in commit-based metrics is the commit interval τ—the elapsed time between consecutive commits by the same author—which leads directly to the notion of commit frequency, defined as its multiplicative inverse. The empirical probability density function (EPDF) f(τ) and its integral, the empirical cumulative distribution function (ECDF), offer statistical descriptions of these intervals across projects and developers. For a given threshold t,

$\mathrm{CDF}(t) = \int_0^t f(\tau)\,d\tau$

represents the probability that the interval between two commits is less than or equal to t (Kolassa et al., 2014). This framework reveals key aspects of developer rhythm: the median commit interval across all projects is approximately 1.666 hours, while the mean (skewed by long inactivity periods) is much higher, at ~3.2 days. Percentile breakdowns—such as the 90th percentile of 4.075 days—further characterize the heavy-tailed nature of commit activity distributions. Individual-level analyses reveal daily commit periodicities manifested as peaks at 24-hour multiples in the EPDF, confirming that many developers adopt a daily commit rhythm.

The invariance of commit frequency distributions across project size strata (small, medium, large) is notable: the maximum difference in ECDFs for median commit intervals across strata was measured at only about 6.3%, indicating that team scale impacts process, but has minimal effect on the temporal structure of individual commit activity (Kolassa et al., 2014).

2. Activity Indicators and Project Vitality Assessment

To operationalize project health, an "activity indicator" is defined as the ratio of the median commit frequency over a defined recent window (e.g., last six months) to that over the entire project lifetime:

$$\textrm{Activity Ratio} = \frac{\textrm{Median Commit Frequency}_{6\textrm{mo}}}{\textrm{Median Commit Frequency}_\textrm{lifetime}}$$

Empirical calibration found that a threshold of approximately 0.47 most effectively distinguishes between active and inactive projects per Daffara’s activity definition (Kolassa et al., 2014). Healthy projects maintain or exceed their historical commit frequency in the recent window, while declining or stagnant projects see this ratio approach zero. This metric supports resource allocation, capacity planning, and early detection of project dormancy.

3. Granularity, Robustness, and Stability Metrics

Temporal aggregation choices critically affect the stability and interpretability of commit-based metrics. Daily commit counts produce bursty measures that rarely reflect real stability (with only 2% of sampled projects exhibiting daily commit stability (Adejumo et al., 4 Aug 2025)), whereas weekly aggregation yields significantly reduced high-frequency noise and allows up to 29% of projects to be identified as stable. The composite stability index (CSI) framework employs a control-theoretic lens, using the coefficient of variation (CV) of commit frequency (ideally μ = 0.25, tolerance σ = 0.25), and a triangular normalization function:

$$\varphi_c(x) = \begin{cases} 1 - \frac{|x - 0.25|}{0.25}, & 0 \leq x \leq 0.5 \ 0, & \text{otherwise} \end{cases}$$

where x is the CV over the chosen time window (Adejumo et al., 4 Aug 2025, Adejumo et al., 2 Aug 2025). Projects with regular, predictable commit rhythms (low variance, moderate mean) are viewed as more stable and resilient, attributes that correlate with mature governance and robust development processes.

4. Commit Classification and Maintenance Profiling

Advanced commit-based metrics extend beyond interval analysis to classify the intent of changes—typically into corrective (bug fixing), perfective (refactoring/improvement), and adaptive (feature addition) maintenance. Compound models leverage both keyword features from commit messages and fine-grained source code change types (e.g., statement_insert, method_removed), utilizing approaches such as Random Forest compound classifiers. When commit messages lack informative keywords, the model falls back to source code change features alone. Feature vectors can reach up to 68 dimensions (20 keywords + 48 code change types) (Levin et al., 2017, Levin et al., 2019). Density metrics—ratio of net (functional) to gross (total) lines changed—further augment precision, elevating cross-project classification accuracy to 89% (Kappa 0.82) compared to 13–17% lower rates with size-only features (Hönel et al., 2020).

Maintenance activity distribution is then used for developer profiling, team allocation, and anomaly detection. Visualization tools (e.g., Software Maintenance Activity Explorer) provide stacked bar charts for tracking the balance and temporal evolution of maintenance types, supporting process improvement and quality assurance (Levin et al., 2019).

5. Social Dynamics, Burstiness, and Activity Cascades

Temporal analysis of commits reveals that OSS developer activity is inherently bursty, characterized by short intervals of intense contribution followed by long periods of inactivity. Burstiness is quantified as

$$B = \frac{\sigma_\tau - \mu_\tau}{\sigma_\tau + \mu_\tau}$$

with B ≈ 0.54 at project level and B ≈ 0.36 at developer level in large OSS datasets (Qarkaxhija et al., 30 Sep 2025). Co-editing network models interpret cascading bursts: a commit by developer A targeting code from developer B, followed by a rapid response from B, constitutes an activity cascade—detected when response intervals fall below the 25th percentile of historical inter-commit times.

Statistical validation via temporal shuffling demonstrates that cascades are not artifacts, but significant social phenomena driving responsiveness and retention. Cascade-derived features, such as average inactivity among neighbors, serve as predictors of developer churn with higher predictive power than raw commit counts (Qarkaxhija et al., 30 Sep 2025).

6. Controlling for Automation and Anomaly Filtering

Automated commits by bots distort traditional commit activity metrics and must be identified and filtered to avoid overestimating productivity or misconstruing developer behavior. The BIMAN framework detects bots through:

• Author name patterns (BIN),
• Message template scores ($\mathrm{Score} = 1 - \frac{|T|}{|D|}$, where |T| is template count and |D| is total messages),
• Commit association features (file extensions, project count, etc.) (Dey et al., 2020).

Ensemble methods (Random Forest) yield AUC-ROC ~0.9. Filtering bot commits enables recalibration of valid human effort and more faithful metrics for both individual and team-level performance analysis.

Complementing bot filtering, statistical frameworks such as mean-high Model Contribution Rate (mhMCR) discard low-contribution and anomalous commits by focusing on the mean of contributions in the upper quartile, after removing outliers via IQR filtering. This yields metrics less sensitive to mass refactorings or infrequent, large commits (Bishop et al., 8 Dec 2024).

7. Implications for Practice, Risk, and Project Health

Commit-based activity metrics underpin a variety of operational and strategic decisions. Systems and dashboards aggregate and visualize commit-derived indicators (e.g., project size trajectories, productivity, defect density) at granularities from daily to monthly for capacity planning and team coordination (Thiruvathukal et al., 2018). Composite frameworks such as the CSI aggregate commit stability, issue and PR responsiveness, and community engagement into holistic risk signals, informing supply chain health, onboarding prioritization, and dependency selection (Adejumo et al., 2 Aug 2025, Adejumo et al., 4 Aug 2025).

Empirical studies confirm that high annual commit throughput does not necessarily imply temporal stability, nor resilience. Instead, regularity and predictability of commit activity—augmented with auxiliary process metrics—are superior proxies for long-term project viability and maintenance risk. For OSS communities, monitoring burstiness and cascade participation enables proactive retention strategies and supports sustainability (Qarkaxhija et al., 30 Sep 2025).

In summary, commit-based activity metrics, when robustly filtered, accurately classified, and properly contextualized, provide a rigorous quantitative foundation for understanding, benchmarking, and improving software project dynamics across individual, team, and organizational boundaries.