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Identifying relevant Factors of Requirements Quality: an industrial Case Study

Julian Frattini

TL;DR

This study tackles the practical question of which requirements quality factors truly matter in industry. It adopts an Ericsson case study with dual data sources—engineer interviews and requirement-engineering issue reports—analyzed through thematic synthesis grounded in the Requirements Quality Theory (RQT). The authors identify 17 relevant quality factors and 11 interaction effects (including factor–factor and factor–context interactions) and derive actionable propositions to advance RQT and NaPiRE-based research. Practically, the work narrows the expansive space of quality factors to a core, context-sensitive set and demonstrates how context mediates the impact of quality defects, enabling more robust, proactive RE practices.

Abstract

[Context and Motivation]: The quality of requirements specifications impacts subsequent, dependent software engineering activities. Requirements quality defects like ambiguous statements can result in incomplete or wrong features and even lead to budget overrun or project failure. [Problem]: Attempts at measuring the impact of requirements quality have been held back by the vast amount of interacting factors. Requirements quality research lacks an understanding of which factors are relevant in practice. [Principal Ideas and Results]: We conduct a case study considering data from both interview transcripts and issue reports to identify relevant factors of requirements quality. The results include 17 factors and 11 interaction effects relevant to the case company. [Contribution]: The results contribute empirical evidence that (1) strengthens existing requirements engineering theories and (2) advances industry-relevant requirements quality research.

Identifying relevant Factors of Requirements Quality: an industrial Case Study

TL;DR

This study tackles the practical question of which requirements quality factors truly matter in industry. It adopts an Ericsson case study with dual data sources—engineer interviews and requirement-engineering issue reports—analyzed through thematic synthesis grounded in the Requirements Quality Theory (RQT). The authors identify 17 relevant quality factors and 11 interaction effects (including factor–factor and factor–context interactions) and derive actionable propositions to advance RQT and NaPiRE-based research. Practically, the work narrows the expansive space of quality factors to a core, context-sensitive set and demonstrates how context mediates the impact of quality defects, enabling more robust, proactive RE practices.

Abstract

[Context and Motivation]: The quality of requirements specifications impacts subsequent, dependent software engineering activities. Requirements quality defects like ambiguous statements can result in incomplete or wrong features and even lead to budget overrun or project failure. [Problem]: Attempts at measuring the impact of requirements quality have been held back by the vast amount of interacting factors. Requirements quality research lacks an understanding of which factors are relevant in practice. [Principal Ideas and Results]: We conduct a case study considering data from both interview transcripts and issue reports to identify relevant factors of requirements quality. The results include 17 factors and 11 interaction effects relevant to the case company. [Contribution]: The results contribute empirical evidence that (1) strengthens existing requirements engineering theories and (2) advances industry-relevant requirements quality research.
Paper Structure (32 sections, 4 figures, 3 tables)

This paper contains 32 sections, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Requirements Quality
  4. Requirements Quality Theory
  5. Gap
  6. Method
  7. Data Collection
  8. Interview Data
  9. Issue Data
  10. Data Analysis
  11. Data extraction
  12. Data coding
  13. Theme identification
  14. Model creation
  15. Trustworthiness Assessment
  16. ...and 17 more sections

Figures (4)

  • Figure 1: Groups and concepts of the reduced Requirements Quality Theory frattini2023requirements
  • Figure 2: Overview of the data collection and analysis method
  • Figure 3: Model of activities (as classes) and attributes (as their attributes)
  • Figure 4: Interaction effect between solution-orientation and involvement on the understanding activity