Differentiating Between New And Existing Bugs: Best Practices For Agile Teams

Identifying Bug Origins

When a new bug is reported, the first step is to identify its origin. This involves reviewing recent code changes, checking if the issue replicates in previous versions, and understanding the difference between the intended and actual behavior of the affected feature.

Reviewing Commit History for Recent Changes

Using version control system logs, developers can review commits made in areas of code related to the bug report. Scanning commit messages and changed files can reveal recent modifications likely to have introduced new issues. Reverting suspect commits to test if the bug persists on older code can confirm if they are the root cause.

Checking if Issue Replicates in Previous Versions

Attempting to recreate the bug in earlier versions and environments quickly indicates if it existed prior to recent work. If the issue only surfaces in newer iterations, it points to new code or configuration changes causing the unexpected behavior. Legacy defects reveal areas needing additional test coverage and improved understanding of impacts from existing logic.

Understanding Intended vs Actual Behavior

When bugs emerge from new development, programmers should document the expected functionality that is not occurring. For legacy issues, expected behavior may require input from product experts and managers. Comparing intended vs actual outputs highlights technical gaps and misunderstood requirements needing correction.

Reproducing Issues

Reliably recreating software issues is crucial for identifying root causes and evaluating fixes. It involves clearly documenting steps which trigger the defect, capturing debugging data from affected runs, and determining if bug reproduction depends on specific environments.

Following Steps to Recreate Bug

Having end users or quality engineers provide distinct step-by-step directions to manifest a defect introduces consistency into reproducing it. When reproduction is unreliable, investigating the code path and correcting obscurities in the steps provides firmer ground for diagnosis and testing of corrections.

Capturing Debugging Information

Inspecting program state when defects occur is key to narrowing down culprit code. Recording error logs, system metrics, application logs, screen captures, database queries, network calls, and other runtime data during reproduction supports detailed analysis of potentially faulty areas.

Determining if Bug Exists in Other Environments

Attempting to manifest issues in various environments indicates external contributing factors. Reproductions dependent on specific servers, software versions, browsers, devices, or other environment elements help identify additional dimensions needing adjustment alongside core code fixes.

Prioritizing Investigation

With finite resources, critical bugs requiring immediate investigation must be differentiated from less severe defects. Factors like application scope, usage levels, root cause complexity, and fix timelines all inform investigation urgency and scheduling.

Considering Application Scope and Usage

Bugs affecting central interfaces used daily by large internal or customer audiences cause greater damage than issues in peripheral modules accessed infrequently. Defects around financial transactions or regulatory processes also merit prioritized work over those impacting internal websites.

Estimating Effort to Fix Based on Root Cause

Once reproduction steps are confirmed, preliminary diagnosis work determines where bugs originate and anticipated correction effort. Defects requiring significant architectural changes, long testing cycles, or large code refactoring may force postponement compared to simpler fixes executable in the current sprint.

Weighing Benefits of Fixing vs Other Work

Product owners must evaluate if addressing a defect outweighs planned feature work for an iteration. Bugs not fully understood or requiring lengthy fixes may be candidates for backlogs, while serious defects blocking major functionality are handled urgently.

Confirming Fixes

After code corrections, sufficient testing validates if bugs are fully resolved across environments. Confirming fixes before closing issues prevents premature sign-offs, ensuring stakeholder sign-off.

Writing Targeted Unit and Integration Tests

Unit tests exercising corrected code paths validate local function. Integration tests then confirm modules interact properly after isolated fixes. Together, targeted validating correct behavior at code and subsystem levels.

Testing Bug Scenario in Dev and Staging Environments

Running test cases which previously reproduced defects in development, QA, and staging landscapes ensure problems do not re-emerge closer to production. Dev and stage tests also build confidence in deployment readiness.

Getting Confirmation from Reporters Before Closing

Before officially closing repaired issues, submit fixes to initial reporters for confirmation. End-user validation in native environments ensures corrections apply broadly, preventing premature sign-off.

Preventing Regressions

To avoid reintroduced defects, leverage automation, documentation, and code analysis to extend fixes beyond isolated areas.

Adding Tests to Automated Test Suites

Expand regression testing coverage through new unit, integration, system, and user acceptance tests exercising corrected logic on every code change. Tests also clearly signal if related areas later regress.

Documenting Bugs in Internal Wikis

Central internal wikis recording fixed defects, diagnosis details, and correction methods equip future teams to avoid similar issues. Document key learnings, code bases affected, and recommendations for related modules.

Reviewing Related Areas of Code for Similar Issues

Assess modules sharing characteristics with corrected code for potential latent defects. Apply improved validation rules and parameter checks used to address original bug more broadly where relevant.

Continuous Improvement

Leveraging major bugs as learning opportunities through fix reflections and updated development practices prevents larger regressions.

Performing Root Cause Analysis on Major Bugs

For significant resolved issues, formal root cause analysis details environmental risks, process gaps, testing limits, and training needs contributing to defects. Preventative action improves systemic quality capabilities.

Reflecting on Bugs During Sprint Retrospectives

Review defects from concluded iterations during retrospectives to spur team discussion of process adjustments needed to prevent reoccurrences. Adjust workflows, environments, and testing strategy based on findings.

Updating Coding Guidelines Based on Findings

Where common anti-patterns enable bugs, introduce new programming standards and code review checks to enforce improved techniques, scope validation, and hazard avoidance.

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