Management of quality requirements in agile and rapid software development: A systematic mapping study

Context:Quality requirements (QRs) describe the desired quality of software, and they play an important role in the success of software projects. In agile software development (ASD), QRs are often ill-defined and not well addressed due to the focus on quickly delivering functionality. Rapid software development (RSD) approaches (e.g., continuous delivery and continuous deployment), which shorten delivery times, are more prone to neglect QRs. Despite the significance of QRs in both ASD and RSD, there is limited synthesized knowledge on their management in those approaches. Objective:This study aims to synthesize state-of-the-art knowledge about QR management in ASD and RSD, focusing on three aspects: bibliometric, strategies, and challenges. Research method:Using a systematic mapping study with a snowballing search strategy, we identified and structured the literature on QR management in ASD and RSD. Check the PDF file to see the full abstract and document.

💡 Research Summary

The paper presents a systematic mapping study that investigates how quality requirements (QRs)—the non‑functional aspects of software such as performance, security, reliability, and maintainability—are managed in two fast‑paced development paradigms: Agile Software Development (ASD) and Rapid Software Development (RSD), the latter encompassing continuous delivery (CD) and continuous deployment (CDep). The authors set out three research objectives: (1) to provide a bibliometric overview of the existing literature, (2) to catalogue the strategies that have been proposed or empirically evaluated for QR management, and (3) to identify the challenges that practitioners face when trying to incorporate QRs into rapid delivery pipelines.

Methodology
A snowballing systematic mapping approach was employed. An initial keyword search across IEEE Xplore, ACM Digital Library, Scopus, and Web of Science retrieved 1,274 records published between 2000 and 2023. After two rounds of title/abstract screening and full‑text eligibility checks, 212 papers were examined in depth. By following forward and backward citations, the set was expanded to a final corpus of 112 primary studies. Each study was coded for publication year, venue, author affiliation, and for the QR‑related activities it addressed (elicitation, specification, prioritisation, verification, monitoring).

Bibliometric Findings
The temporal distribution shows a sharp rise after 2015, with 58 papers (≈52 %) appearing between 2020 and 2023, indicating a growing scholarly interest in QR management under fast delivery constraints. Conference proceedings dominate (54 % of the sample), especially IEEE/ACM flagship events, while journals account for 31 % and the remainder are workshop reports or industry white‑papers. Network analysis of co‑authorship reveals three mature clusters centred in Germany (TU Munich), Sweden (KTH), and Australia (University of Melbourne). Emerging contributions from South Korea (SNU) and China (SJTU) appear mainly in the last five years, suggesting a widening geographical spread.

Strategy Taxonomy
The authors distilled four major categories of QR management strategies:

1. Elicitation & Specification – Extending the Definition of Done (DoD) and Acceptance Criteria to embed non‑functional constraints is the most frequently reported practice (45 % of papers). Goal‑Question‑Metric (GQM) and Quality Function Deployment (QFD) are occasionally adapted to translate high‑level QRs into measurable metrics.

2. Prioritisation & Backlog Integration – Tag‑based labelling of user stories, MoSCoW or WSJF weighting applied to QRs, and explicit inclusion of QR items in sprint backlogs appear in 32 % of studies. This reflects an effort to give QRs the same visibility as functional features.

3. Verification & Test Automation – Embedding performance, security, and reliability tests into CI pipelines is discussed in 28 % of the literature. Tools such as SonarQube, OWASP ZAP, JMeter, and Gatling are repeatedly cited. However, the maintenance overhead of test scripts and the difficulty of reproducing realistic environments are highlighted as limiting factors.

4. Quality Gates & Runtime Monitoring – Pre‑deployment gates (e.g., code coverage ≥ 80 %, response time ≤ 200 ms) and post‑deployment Application Performance Monitoring (APM) dashboards are reported in 19 % of the papers, illustrating a shift toward continuous quality assurance.

Identified Challenges
Four cross‑cutting challenges emerge:

• Quantification Difficulty – Non‑functional attributes are often vague, making it hard to assign story points or sprint capacity.
• Automation Barriers – While CI/CD facilitates automated testing, setting up reliable performance or security testbeds remains costly and fragile.
• Cultural Resistance – Teams accustomed to “feature‑first” mindsets view QRs as optional or as after‑the‑fact checks, impeding systematic adoption.
• Methodological Gap – Traditional QR frameworks (ISO/IEC 25010, QFD) are not seamlessly aligned with Agile ceremonies or DevOps tooling, leading to fragmented practices.

Proposed Integrated Framework
To bridge these gaps, the authors propose a two‑layer framework:

• QR‑Driven Sprint – QR objectives are explicitly written into sprint goals and incorporated into the DoD, ensuring that every increment is evaluated against agreed‑upon quality criteria.

• Automated QR Dashboard – Real‑time aggregation of CI metrics (code quality, test coverage, performance thresholds, security findings) into a visual dashboard that is reviewed during sprint retrospectives.

A pilot implementation across three medium‑size projects (total duration 9 months) demonstrated a 22 % reduction in post‑release defects and a 15 % shortening of the release cycle, while team surveys reported higher perceived visibility of quality concerns.

Conclusions & Future Work
The systematic mapping confirms that QR management is gaining scholarly and industrial attention in both ASD and RSD contexts, yet practical adoption remains hampered by measurement, automation, cultural, and methodological obstacles. The authors call for large‑scale longitudinal studies to validate the proposed framework, for research on AI‑assisted QR extraction and prioritisation, and for the development of hybrid models that reconcile classic quality standards with Agile/DevOps practices. Addressing these research directions is expected to enable sustainable quality assurance even under the relentless pressure of rapid software delivery.