Search arXiv⌕ Search

arXiv · 1505.00922

Happy software developers solve problems better: psychological measurements in empirical software engineering

Abstract

For more than 30 years, it has been claimed that a way to improve software developers' productivity and software quality is to focus on people and to provide incentives to make developers satisfied and happy. This claim has rarely been verified in software engineering research, which faces an additional challenge in comparison to more traditional engineering fields: software development is an intellectual activity and is dominated by often-neglected human aspects. Among the skills required for software development, developers must possess high analytical problem-solving skills and creativity for the software construction process. According to psychology research, affects-emotions and moods-deeply influence the cognitive processing abilities and performance of workers, including creativity and analytical problem solving. Nonetheless, little research has investigated the correlation between the affective states, creativity, and analytical problem-solving performance of programmers. This article echoes the call to employ psychological measurements in software engineering research. We report a study with 42 participants to investigate the relationship between the affective states, creativity, and analytical problem-solving skills of software developers. The results offer support for the claim that happy developers are indeed better problem solvers in terms of their analytical abilities. The following contributions are made by this study: (1) providing a better understanding of the impact of affective states on the creativity and analytical problem-solving capacities of developers, (2) introducing and validating psychological measurements, theories, and concepts of affective states, creativity, and analytical-problem-solving skills in empirical software engineering, and (3) raising the need for studying the human factors of software engineering by employing a multidisciplinary viewpoint.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Daniel Graziotin, Xiaofeng Wang, Pekka Abrahamsson. 2015-05-05. Happy software developers solve problems better: psychological measurements in empirical software engineering. https://doi.org/10.7717/peerj.289

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

DynamiQ: Unlocking the Potential of Dynamic Task Allocation in Parallel Fuzzing

We present DynamiQ, a full-fledged and optimized successor to AFLTeam that supports dynamic and adaptive parallel fuzzing. Unlike most existing approaches that treat individual seeds as tasks, DynamiQ leverages structural information from the program's call graph to define tasks and continuously refines task allocation using runtime feedback. This design significantly reduces redundant exploration and enhances fuzzing efficiency at scale. Built on top of the state-of-the-art LibAFL framework, DynamiQ incorporates several practical optimizations in both task allocation and task-aware fuzzing. We evaluate DynamiQ on eighteen targets from Magma and FuzzBench with a total budget of over 40 CPU years. It achieves the highest mean branch coverage on fourteen targets, with average relative gains ranging from 8.37% to 19.53% over five baseline fuzzers. Across our experiments, DynamiQ identifies nine previously unknown bugs.

cs.SE↗

Measuring the Permission Gate: A Stress-Test Evaluation of Claude Code's Auto Mode

Claude Code's auto mode is the first deployed permission system for AI coding agents, using a two-stage transcript classifier to gate dangerous tool calls. Anthropic reports a 0.4% false positive rate and 17% false negative rate on production traffic. We present the first independent evaluation of this system on deliberately ambiguous authorization scenarios, i.e., tasks where the user's intent is clear but the target scope, blast radius, or risk level is underspecified. Using AmPermBench, a 128-prompt benchmark spanning four DevOps task families and three controlled ambiguity axes, we evaluate 253 state-changing actions at the individual action level against oracle ground truth. Our findings characterize auto mode's scope-escalation coverage under this stress-test workload. The end-to-end false negative rate is 81.0% (95% CI: 73.8%-87.4%), substantially higher than the 17% reported on production traffic, reflecting a fundamentally different workload rather than a contradiction. Notably, 36.8% of all state-changing actions fall outside the classifier's scope via Tier 2 (in-project file edits), contributing to the elevated end-to-end FNR. Even restricting to the 160 actions the classifier actually evaluates (Tier 3), the FNR remains 70.3%, while the FPR rises to 31.9%. The Tier 2 coverage gap is most pronounced on artifact cleanup (92.9% FNR), where agents naturally fall back to editing state files when the expected CLI is unavailable. These results highlight a coverage boundary worth examining: auto mode assumes dangerous actions transit the shell, but agents routinely achieve equivalent effects through file edits that the classifier does not evaluate.

cs.SE↗

A Multi-Dimensional, Per-Pass Empirical Study of the LLVM Optimization Pipeline

Quantifying the marginal impact of individual optimization passes underpins phase ordering, pass selection, optimization design, and analysis of pass/hardware interactions. In LLVM, the standard backend for C/C++, Rust, and ML stacks via MLIR, interactions among passes, measurement noise, and pipeline scale make this difficult. We present a systematic empirical study of the LLVM -O3 pipeline. We decompose it into 113 cumulative per-pass prefixes and measure execution time, compile time, binary size, hardware counters, and RAPL energy on 30 PolyBench/C kernels (84,750 measurements). Transitions whose binary is byte-identical to their predecessor give us a built-in null distribution, which we use to calibrate every significance claim. On these compute-bound affine kernels, the pipeline is non-monotone: 36% of the transitions that significantly change runtime are regressions, found in 28 of 30 kernels. Gains are back-loaded (the median kernel needs 84.8% of the pipeline for 80% of its speedup) and concentrated in a small core of passes, a result that holds under both absolute and scale-free impact measures. IR instruction count is an unreliable runtime predictor, and runtime-targeted passes are de facto energy-targeted (30-60% savings). Finally, we bound the speedup lost to non-monotone behavior from above by 46.35% and decompose it: 9.7 points are measurement noise, 15.3 points are transient dips repaired within three passes, and only 21.3 points persist to the end of the pipeline. These findings inform pass pruning, cost-model calibration, and autotuning.

cs.SE↗