Research:Question-01-Factor-Performance-Correlation

Research Question 01: How do the 10 success factors correlate with actual job performance across different developer experience levels?

Summary

This research question examines the correlation between key developer success factors and actual job performance across different experience levels. Through analysis of multiple empirical studies, the evidence reveals complex relationships between technical skills, communication abilities, team dynamics, and individual performance metrics. Notably, recent studies on AI coding tools provide unprecedented insights into how experience levels interact with productivity measures in modern development environments.

Research Question

How do the 10 success factors correlate with actual job performance across different developer experience levels?

This question seeks to understand which developer characteristics most strongly predict job success, how these correlations vary across experience levels, and what empirical evidence exists to support or challenge traditional assumptions about developer effectiveness.

Background and Motivation

The software development industry has long debated which factors most reliably predict developer success and productivity. Traditional assumptions about the primacy of technical skills and experience have been challenged by recent empirical research, particularly studies examining how AI coding tools interact with developer capabilities at different experience levels.

Research Findings from Literature

Primary Empirical Studies

Critical Success Factors Research

Springer Software Quality Journal (2018) - Major empirical study involving 101 software projects in the Turkish software industry:

"The top three CSFs having the most significant associations with project success were: (1) project team's experience with the software development methodologies, (2) project team's expertise with the task, and (3) project monitoring and controlling."

Key Finding on Experience vs Management:

"Project monitoring and controlling and project planning were ranked even higher than the team's experience with development methodologies. The research suggests that while technical abilities of software engineers are important, project management seems to be even more important."

Source: Correlation of critical success factors with success of software projects: an empirical investigation

Group-Level Performance Analysis

ScienceDirect (1993) - Analysis of 31 software development groups examining cohesiveness, experience, and capability:

"Using data from 31 software development groups, researchers examined the influence of the group's cohesiveness, total experience in software development and capability on the group's performance level. The influence of cohesiveness and capability was found to be strong and significant while the influence of experience was the weakest."

Source: Understanding the factors influencing the performance of software development groups: An exploratory group-level analysis

Technical vs Non-Technical Skills Framework

ScienceDirect (2025) - Comprehensive framework study with 158 participants:

"Recent research has developed frameworks that evaluate developers' technical and non-technical skills separately using collected data and computes their respective indices to derive an overall measure of code comprehension ability. An empirical study with 158 participants assessed technical skills, including code understanding, debugging, and completion, alongside non-technical skills such as problem-solving, emotions, long-term memory, belief, desire, intention, and commitment."

Non-Technical Skills Impact:

"Empirical studies highlight the significance of non-technical skills, such as problem-solving, emotions, long-term memory, belief, desire, intention, and commitment, in shaping a developer's approach to code comprehension. Problem-solving is crucial for understanding how different parts of the code interact and for devising strategies to resolve issues or optimize performance."

Source: A novel framework for evaluating developers' code comprehension proficiency through technical and non-technical skills

AI Tools and Experience Level Studies

METR Study (2025) - Experienced Developer Performance

Randomized Controlled Trial with 16 Experienced Developers:

"A recent randomized controlled trial by METR studied AI tools' impact on software development productivity, conducting research with 16 experienced developers with moderate AI experience completing 246 tasks in mature projects on which they had an average of 5 years of prior experience."

Surprising Performance Results:

"The most surprising finding was that allowing AI actually increased completion time by 19%—AI tooling slowed developers down, despite developers' expectations. Before starting tasks, developers forecast that allowing AI would reduce completion time by 24%, and after completing the study, developers estimated that allowing AI reduced completion time by 20%."

Source: Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity

Junior vs Senior Developer AI Performance

Multiple Industry Studies on Experience-Performance Paradox:

"Junior-level developers saw productivity boosts of 21% to 40%, while long-tenure and senior developers saw more modest gains of 7% to 16%. This suggests that AI coding assistants could be a powerful tool for onboarding new developers, accelerating the productivity ramp-up for new hires, and potentially narrowing the productivity gap between junior and senior developers."

Complex Task Performance:

"Time savings can vary significantly based on task complexity and developer experience. Time savings shrank to less than 10 percent on tasks that developers deemed high in complexity due to, for example, their lack of familiarity with a necessary programming framework."

Source: AI coding tools may not speed up every developer, study shows

Communication and Collaboration Factors

Communication as Critical Success Factor:

"Communication has been recognized as an important factor for success in software development projects because previous researches on stakeholder analysis and collaboration has demonstrated the importance of communication. Developers often complained about difficulties in collaboration, communication breakdowns, unresponsive team members, and interpersonal conflicts."

Social and Human Factors Impact:

"Research aims to know if software engineering professionals consider that social and human factors (SHF) influence the productivity of a work team. Empirical results show professionals agree with the SHF in the context of software development influence in the productivity of work teams."

Source: Factors Affecting Software Developer's Performance

Analysis: Relationship to Original 10-Factor Framework

Strong Empirical Support

The literature provides substantial support for several factors in the original 10-factor framework:

1. Context Retention: The METR study's finding that experienced developers slow down with AI tools aligns with the importance of context retention - experienced developers may struggle because AI disrupts their established context-management workflows.

2. Strategic Thinking: The Turkish software industry study's finding that "project monitoring and controlling" ranked higher than technical experience directly supports the strategic thinking factor's importance.

3. Communication Skills: Multiple studies cite communication breakdowns as major productivity inhibitors, strongly supporting this factor's inclusion.

4. Collaboration: The group dynamics study showing cohesiveness as more important than individual experience validates team collaboration factors.

Contradictions and Surprises

Experience Paradox: The most significant contradiction is the finding that experience was "the weakest" factor in group performance studies, and that experienced developers actually perform worse with AI tools. This challenges traditional assumptions about experience being a primary success predictor.

Technical vs Management Skills: The finding that project management capabilities often outweigh pure technical abilities suggests a need to weight strategic and organizational factors higher than initially conceptualized.

Framework Expansions

The empirical research suggests expanding the original framework to include:

1. Emotional Intelligence: Studies emphasizing "emotions, long-term memory, belief, desire, intention, and commitment" suggest psychological factors deserve more prominence.

2. Adaptation Capacity: The AI tool studies suggest that adaptability to new technologies may be more important than baseline technical skill.

3. Task Context Sensitivity: Research showing performance varies dramatically based on task complexity suggests context-dependent factor weighting.

Conclusions

The empirical literature reveals that developer success factors show complex, non-linear relationships with performance that vary significantly by experience level and context. The most striking finding is the "experience paradox" - that traditional experience metrics may be less predictive of success than previously assumed, particularly in AI-augmented development environments.

Key validated factors include strategic thinking, team collaboration, communication skills, and surprisingly, project management capabilities. The research suggests that soft skills and adaptability may be more predictive of long-term success than pure technical proficiency.

Sources and References

1. Correlation of critical success factors with success of software projects: an empirical investigation - Software Quality Journal
2. Understanding the factors influencing the performance of software development groups - ScienceDirect
3. A novel framework for evaluating developers' code comprehension proficiency - ScienceDirect
4. Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity - METR
5. AI coding tools may not speed up every developer, study shows - TechCrunch
6. Factors Affecting Software Developer's Performance - ResearchGate

See Also

• Research Question 3: Predictive Success Indicators
• Research Question 6: Team Composition and Diversity Effects
• Research Question 13: AI Benchmark Accuracy Assessment
• Idea:10-Factor Developer Success Model
• Research:AI-Human Development Continuum Investigation