Literature review
AuraScore 81/100

Developer Productivity Framework Systematic Literature Project Plan

Coordinate a mixed-methods literature review plan bridging academic software engineering research and industry gray literature.

Execute this protocol when building evidence-backed developer experience strategies based on peer-reviewed studies and industry reports. It structures extraction criteria, quality scoring, and organizational synthesis.

Template

Role: Head of Developer Experience (DevEx) Research and Staff Socio-Technical Systems Analyst.

Context

  • Research Topic: {{productivity_framework_topic}}
  • Empirical Methods Scope: {{empirical_study_methodologies}}
  • Non-Academic Sources: {{gray_literature_sources}}
  • Target Developer Cohort: {{developer_cohort_profile}}
  • Evaluation Rubric: {{quality_assessment_rubric}}
  • Program Milestones: {{strategic_rollout_milestones}}

Task

Formulate a rigorous mixed-methods literature review and evidence synthesis project plan that aggregates academic software engineering literature and practitioner gray literature to construct an actionable developer productivity model for {{developer_cohort_profile}}.

Method

  1. Define primary research questions investigating cognitive load, feedback loops, engineering velocity, and satisfaction metrics.
  2. Design a multi-vocal search strategy integrating academic engines (ACM/IEEE/Springer) and practitioner sources defined in {{gray_literature_sources}}.
  3. Establish source-specific qualification criteria separating rigorous practitioner whitepapers from non-reproducible promotional content.
  4. Apply {{quality_assessment_rubric}} across identified empirical software engineering studies to weight evidence credibility.
  5. Construct a qualitative coding framework (using inductive and deductive coding) to extract themes around friction points and interventions.
  6. Formulate a synthesis model harmonizing quantitative telemetry metrics with subjective perceptual surveys.
  7. Map synthesized findings directly against the socio-technical constraints of {{developer_cohort_profile}}.
  8. Establish governance and milestone execution gates aligning with {{strategic_rollout_milestones}}.

Constraints

  • MUST utilize a dual-track appraisal protocol that separately scores peer-reviewed empirical papers and industry gray literature.
  • MUST NOT recommend interventions that lack empirical triangulation across at least two independent studies.
  • All qualitative extraction codes must be cross-referenced against validated constructs within {{productivity_framework_topic}}.
  • Synthesis outputs must separate correlation from verified causal relationships in surveyed interventions.

Output format

Provide the complete review project plan organized into five markdown sections:

  1. Literature Review Charter & Research Objectives (clarifying questions, scope, and target outcomes)
  2. Dual-Track Search & Retrieval Protocol (academic Boolean queries and practitioner screening heuristics)
  3. Methodological Quality Appraisal Framework (evaluation scoring cards based on {{quality_assessment_rubric}})
  4. Qualitative & Quantitative Synthesis Schema (thematic coding structure and metric triangulation matrix)
  5. Execution Roadmap & Leadership Milestone Schedule (phased activities mapped to {{strategic_rollout_milestones}})

Self-review

  • Verify that the plan explicitly differentiates data extraction protocols for academic papers versus {{gray_literature_sources}}.
  • Ensure the synthesis schema directly addresses the unique operational context of {{developer_cohort_profile}}.
  • Check that the quality appraisal section specifies transparent threshold scores for study inclusion.
AuraScore breakdown
81/100Provisional
Instruction clarity15/15 · Strong

Explicit role, a named task, and discrete steps the model can follow.

Context architecture12/12 · Strong

Background, inputs and variables the model needs before it starts.

Constraint engineering12/12 · Strong

Hard boundaries — what the model must and must not do.

Output specification6/14 · Thin

A named, field-level shape for the response.

Reasoning structure10/10 · Strong

Ordered work items that force analysis before an answer.

Model compatibility10/10 · Strong

Length and structure that travel across frontier models.

Token efficiency5/10 · Thin

Signal density — instruction weight without padding.

Reusability7/7 · Strong

Documented variables so the scaffold adapts to new inputs.

Robustness3/5 · Adequate

Quality bar, assumptions and behaviour when inputs are thin.

Observed performance1/5 · Thin

How much real usage the template has behind it.

research-analysis
research-literature
technology-software
devex
developer-productivity
mixed-methods