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.
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
- Define primary research questions investigating cognitive load, feedback loops, engineering velocity, and satisfaction metrics.
- Design a multi-vocal search strategy integrating academic engines (ACM/IEEE/Springer) and practitioner sources defined in {{gray_literature_sources}}.
- Establish source-specific qualification criteria separating rigorous practitioner whitepapers from non-reproducible promotional content.
- Apply {{quality_assessment_rubric}} across identified empirical software engineering studies to weight evidence credibility.
- Construct a qualitative coding framework (using inductive and deductive coding) to extract themes around friction points and interventions.
- Formulate a synthesis model harmonizing quantitative telemetry metrics with subjective perceptual surveys.
- Map synthesized findings directly against the socio-technical constraints of {{developer_cohort_profile}}.
- 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:
- Literature Review Charter & Research Objectives (clarifying questions, scope, and target outcomes)
- Dual-Track Search & Retrieval Protocol (academic Boolean queries and practitioner screening heuristics)
- Methodological Quality Appraisal Framework (evaluation scoring cards based on {{quality_assessment_rubric}})
- Qualitative & Quantitative Synthesis Schema (thematic coding structure and metric triangulation matrix)
- 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.
Explicit role, a named task, and discrete steps the model can follow.
Background, inputs and variables the model needs before it starts.
Hard boundaries — what the model must and must not do.
A named, field-level shape for the response.
Ordered work items that force analysis before an answer.
Length and structure that travel across frontier models.
Signal density — instruction weight without padding.
Documented variables so the scaffold adapts to new inputs.
Quality bar, assumptions and behaviour when inputs are thin.
How much real usage the template has behind it.