Usefulness in Developer Explanations

• The paper highlights that perceived usefulness is quantified via Likert ratings and behavioral proxies (e.g., normalized upvotes) to assess explanation quality.
• Developer explanations are rated higher when they feature structured content, timely responses, and credible authorship, thereby supporting task goals.
• Integrated LLM tools and tailored explanation strategies significantly boost the perceived usefulness for both novice and expert users.

Perceived usefulness in developer explanations denotes the degree to which developers or related stakeholders assess explanatory artifacts—such as code comments, code-plus-text answers, defect prediction rationales, or AI-generated clarifications—as being actionable, relevant, and supportive of their task goals in software engineering, programming, or requirements communication contexts. This construct is variably operationalized as “usefulness,” “helpfulness,” or “utility,” typically combining subjective self-reports with behavioral proxies (e.g., upvotes, adoption intent, engagement time). Studies draw on foundational models such as the Technology Acceptance Model (TAM) while also analyzing specific measurable attributes within explanation artifacts and their communicative situations (Obaidi et al., 21 Jan 2026, Tambwekar et al., 2023, Figl et al., 27 Aug 2025, MacNeil et al., 2022, Nam et al., 2023, Jiarpakdee et al., 2021).

1. Conceptualization and Operational Measurement

Perceived usefulness is primarily operationalized through Likert-type ratings, behavioral proxies (e.g., upvotes, adoption rates), and sometimes engagement signals (e.g., dwell time, button-presses). In research on Stack Overflow, usefulness has been quantified as the normalized share of upvotes for an answer relative to all answers in a thread:

$$U_{ps,i} = \frac{\text{Score}_i}{\sum_{j\in A(q)}\text{Score}_j}$$

where $\text{Score}_i$ is upvotes minus downvotes for answer $i$, and $A(q)$ is the set of answers to question $q$ (Obaidi et al., 21 Jan 2026). Controlled experiments additionally deploy explicit 7-point or 5-point Likert-scale items such as “How helpful is this answer for solving your problem?” (Figl et al., 27 Aug 2025), or TAM-derived items including “Using this explainable agent would be useful for me” (Tambwekar et al., 2023). In LLM-augmented environments or e-books, in-situ sliders capture statements like “The explanation was useful for me” (MacNeil et al., 2022).

Qualitative rationales are commonly triangulated, with post-task surveys or feedback forms elucidating why explanations were deemed useful or unhelpful, emphasizing attributes such as concreteness, coverage, and context-awareness.

2. Structural, Contextual, and Linguistic Determinants

Large-scale studies consistently find that structural richness, promptness, and author credibility drive perceived usefulness in developer explanations (Obaidi et al., 21 Jan 2026). Key observed determinants include:

• Content and Structure: Presence of code blocks (Spearman’s $\rho = 0.26$), links ($\rho = 0.23$), paragraphs ($\rho = 0.24$), and word/sentence counts ($\rho = 0.19$, $0.17$) yield small, robust positive effects.
• Contextual/Author Factors: Timing ($\rho = -0.50$; lower delay increases usefulness), owner reputation ($\rho = 0.41$), and badge count have moderate or greater impacts. Editor features are negligible (Obaidi et al., 21 Jan 2026).
• Linguistic Features: Sentiment polarity ($\eta = 0.0009$) and readability ($\rho = -0.06$) exert no meaningful influence. That is, clarity and substance matter far more than tone or formal ease-of-reading (Obaidi et al., 21 Jan 2026).

In controlled experiments, both block and inline comments significantly outperform uncommented code in perceived helpfulness (block vs. none: Cohen’s $d \approx 0.78$), with block comments especially benefiting novices (Figl et al., 27 Aug 2025). Natural-language explanation text, whether above code snippets in answers or as generated summaries, is consistently valued for orienting readers (Figl et al., 27 Aug 2025, MacNeil et al., 2022).

3. Explanation Modalities and Visualization Techniques

Distinct modalities of explanation structure yield differing effects on perceived usefulness and downstream utility. In defect prediction and explainable AI:

• Local Model-Agnostic Explanations (LIME): Offers instance-level rationales by approximating complex models locally. LIME is ranked #1 in “usefulness” (76% agree) and is preferred for tracing which feature thresholds push risk scores, though can become unwieldy at scale (Jiarpakdee et al., 2021).
• Global Feature Importance (ANOVA/VarImp): Aggregates feature contributions model-wide (permutation-based or ANOVA), ranked #2 in usefulness and seen as vital for strategic planning but less actionable at the file level (Jiarpakdee et al., 2021).

In human- and AI-authored code explanations, block comments (“top-down beacons”) are preferred by novices, whereas inline comments help clarify operational minutiae (“bottom-up beacons”); natural-language narrative outperforms program or tree-structured pseudo-code for subjective usefulness but not for objective simulation accuracy (Tambwekar et al., 2023).

A dissociation is observed: narrative text explanations maximize subjective usefulness (modified text > trees, $p<.05$), but decision tree and program-style explanations foster accurate mental models for experts (Tambwekar et al., 2023).

4. Audience, Task, and Situational Moderators

The impact of explanation features is moderated by user expertise, prior knowledge, and situational context. For example:

• Expertise: Novices rate block comments as more helpful than inline comments, and benefit more from natural-language narratives (Figl et al., 27 Aug 2025, Tambwekar et al., 2023). For experts, structured trees or programmatic explanations yield higher utility in “simulatability” tasks.
• Prior Knowledge: When users already understand code, added explanations are less helpful (Pearson's $r \approx -0.44$ between prior knowledge and “usefulness for me”) (MacNeil et al., 2022).
• Promptness and Engagement: Early explanations are more valuable. In both Stack Overflow and e-learning, explanations for longer or more complex snippets yield longer engagement times ($r(11) = 0.92, p<.05$) (MacNeil et al., 2022, Obaidi et al., 21 Jan 2026).
• Task Setting: In Q&A sites, surface features such as answer position or upvotes have minimal direct effect on helpfulness perceptions, whereas explanation content and presentation style dominate (Figl et al., 27 Aug 2025).

5. Tool Integration, LLM Explanations, and Practical Recommendations

Integrated tools and AI systems modify the landscape of perceived usefulness.

• LLM-Augmented Developer Tools: In-IDE, context-aware LLM explanations are rated as more useful than generic web search outputs (mean PU for LLM tool = 33.49 vs. 27.30 for search, $p<0.001$) (Nam et al., 2023). Developers favor contextually tailored, minimal-prompt interfaces. Difficulties with prompt engineering and excessive or insufficient granularity can hinder utility.
• Multiplexed Explanation Types: LLM-generated line-by-line, summary, and concept-list explanations all attract engagement; summaries and concept-lists trend slightly higher in usefulness, though sample sizes preclude strong inference (MacNeil et al., 2022). Usability is maximized when systems scaffold explanation exploration, offer multiple abstraction levels, and personalize for user background (Jiarpakdee et al., 2021, Tambwekar et al., 2023, MacNeil et al., 2022).
• Recommendation Strategies: Combining domain-specific visuals with interactive, human-in-the-loop drill-down (global-to-local), surfacing explanations on-demand, and focusing on concrete examples improves perceived usefulness and adoption (Jiarpakdee et al., 2021, Obaidi et al., 21 Jan 2026).

6. Limitations, Validity, and Future Directions

Several studies acknowledge tradeoffs between subjective (perceived) and objective (actual) usefulness:

• Construct Validity: Normalized upvotes and Likert-scale ratings capture attention/appreciation but not correctness, deep understanding, or successful reuse (Obaidi et al., 21 Jan 2026, Figl et al., 27 Aug 2025).
• Experimental Limitations: Simulated Stack Overflow environments, convenience sampling, and instrument reliability (e.g., unreported Cronbach's α) may constrain generalizability (Figl et al., 27 Aug 2025, MacNeil et al., 2022, Nam et al., 2023).
• Confounding: Bivariate correlations do not reveal causality; multivariate and causal inference designs are needed to isolate drivers of perceived usefulness (Obaidi et al., 21 Jan 2026).
• Modality Divergence: Textual explanations optimize comfort/adoption for novices; structured pseudo-code and models optimize simulatability and transfer for experts (Tambwekar et al., 2023).
• Platform and Domain Effects: Findings from Stack Overflow (“android” tag) may not directly transfer to other languages, domains, or chat-based coding platforms (Obaidi et al., 21 Jan 2026).

Future research directions include integrating deeper semantic measures, using A/B or causal-inference studies, deploying in real-world codebases and organizational communication, and rigorously linking perceived usefulness to measurable comprehension and task outcomes (Obaidi et al., 21 Jan 2026, Nam et al., 2023, MacNeil et al., 2022).

7. Synthesis and Implications

Across human- and AI-generated contexts, perceived usefulness in developer explanations is a robust function of explanation structure (code-plus-comment), timing, and author credibility, clearly outweighing stylistic and affective linguistic features. Explanations that provide actionable, concrete artifacts with both global and local interpretability are most likely to be adopted and trusted. Personalization and just-in-time surfacing further optimize this perceived value. However, maximizing perceived usefulness does not guarantee effective understanding or task success; hybrid adaptive systems that reconcile these aspects remain an area of active inquiry (Obaidi et al., 21 Jan 2026, Jiarpakdee et al., 2021, Tambwekar et al., 2023, Figl et al., 27 Aug 2025, MacNeil et al., 2022, Nam et al., 2023).