What are the most effective metrics for bug tracking?

Has to be done with the business to ascertain the severity. QA need to guide the conversation but cannot be the sole team to assign severity.

Pour le suivi efficace des bogues, des métriques clés sont cruciales. Le taux d'ouverture des bogues offre une vue rapide de la qualité du code avec le nombre de problèmes signalés. La mesure du temps moyen de résolution indique l'efficacité de l'équipe à résoudre les problèmes détectés. La gravité des bogues classe l'impact sur l'expérience utilisateur, permettant une priorisation intelligente. Le taux de récidive évalue la fréquence de réapparition des bogues après correction. En parallèle, le taux de fermeture des bogues mesure la rapidité avec laquelle les problèmes sont résolus et clôturés. Ces métriques combinées fournissent une évaluation complète, orientant les efforts vers une amélioration continue de la qualité logicielle.

Different stakeholders (developers, testers, users, business people, ...) might judge severity differently. Typically, it depends on what the severity measure is used for (ranking bug fix order, estimating bug fix effort, ...). Sometimes, severity is confused with urgency.

Bug density should only be measured, if it is used properly. Improper use would be comparing bug density measurements where non-comparable size measures were used. Also, bug density should be classified by severity level.

Bug resolution time (probably effort is meant here) is important for building estimation models that help with resource allocation for newly incoming bugs. If a resolution turns out to not really (or fully) have fixed the bug, it is not always clear how bug resolution time should correctly be measured.

The bug backlog often includes the severity and priority of each bug. If high-severity or high-priority bugs remain unresolved for extended periods, it could indicate a bottleneck in addressing critical issues. This might be due to resource allocation issues, lack of expertise, or inefficiencies in the process. Also, if the backlog consists of minor issues that will never be fixed, and they stay for more than 6 months, we usually move such bugs to the 'obsolete" stage to keep the backlog relevant and short. Make sure you have labels and tags for them. The team can always revisit obsolete items if decide to rework some product component entirely.

It might be had to quantify it, but engineers are normally happy to provide qualitative feedback on the quality of a bug report. Basically, the bug report should help the engineer to identify where the problem is in the shortest amount of time possible. So as a proxy, the contents of a bug report can be used as a KPI, e.g.: Does the bug report have 1) visual proof 2) environment details 3) console / network logs 4) is it easy to reproduce?

In my experience, you should also consider implementing "Signal-Driven Testing", which involves closely monitoring market signals like app store reviews or social media feedback. This approach not only provides direct insights into user experiences but also helps identify patterns and trends in the issues reported. These patterns are invaluable for prioritizing bug fixes and enhancing client satisfaction. Tools like AppFigures, which aggregate reviews from various sources into a single platform are useful in managing this data. Signal-driven testing can significantly improve our understanding of real-world user challenges, making it a good strategy for comprehensive bug tracking.

A couple of additional key metrics are: Defect Aging: The time elapsed between the detection of a defect and its resolution. This helps to assess the efficiency of the defect resolution process and identify bottlenecks. Defect Rejection Rate: Percentage of reported defects that are rejected as invalid or not reproducible. The insight from this metric helps to assess the accuracy of defect reporting and the clarity of communication between testers and developers. Defect Removal Efficiency (DRE): The percentage of defects identified and removed during the testing phase, which helps to measure how well the testing process identifies and eliminates defects before the release. RCA and Defect trends are also meaningful insights.

In my experience, additional metrics such as escaped defects, re-opened defects and defect reported by customer can also help as effective bug metrics to improve the effectiveness of bug fixes.

Some of the most effective metrics for bug tracking include the number of open bugs, bug density (number of bugs per lines of code), mean time to detection (MTTD), and mean time to resolution (MTTR). These metrics can help you track the effectiveness of your bug tracking processes and identify areas for improvement. Additionally, tracking bug severity and prioritizing bugs based on their impact can help you focus on the most critical issues first.

Effective metrics for bug tracking are crucial for understanding the health of a project, identifying problem areas, and improving overall software quality. Here are some of the most effective metrics for bug tracking: 1. Bug Count Metrics 2. Bug Severity and Priority Metrics 3. Bug Resolution Metrics 4. Bug Detection Metrics 5. Bug Closure Metrics 6. Customer-Related Metrics 7. Testing Coverage Metrics