Continuous Quality Assessment of Web Applications through Software Observability
TL;DR
Evaluation on four web applications shows that SoftScanner reduces assessment effort by orders of magnitude, achieves complete agreement with ground truth under the evaluated conditions, and substantially lowers runtime overhead compared to a general-purpose observability baseline.
Abstract
: Understanding how web applications behave at runtime is central to their comprehension and maintenance. Continuous quality assessment supports this understanding by evaluating quality attributes such as performance, usability, and reliability during system operation. While software observability provides the technical foundation for such assessment, its practical realization remains limited by four interrelated challenges: (1) divergent stakeholder perspectives; (2) underspecified mappings between quality goals and the observable data needed to assess them; (3) performance overhead from indiscriminate data collection; and (4) intrusive code modifications for instrumentation. Existing approaches address these challenges separately but lack a unified, goal-driven framework connecting quality goals to the data and mechanisms that operationalize them. This paper introduces SoftScanner , an approach that integrates quality modeling, observability, and automated instrumentation for continuous assessment of frontend web applications. SoftScanner links ISO/IEC-aligned goals to runtime-measurable metrics and observable data (SSQMM), generates lightweight goal-specific instrumentation agents (SAIF), and computes normalized, traceable quality scores (SEQA). Evaluation on four web applications—two industrial and two open-source—shows that SoftScanner reduces assessment effort by orders of magnitude, achieves complete agreement with ground truth under the evaluated conditions, and substantially lowers runtime overhead compared to a general-purpose observability baseline.