Programmers Prefer Individually Assigned Tasks vs. Shared Responsibility
March 22, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Adela Krylova, Roman Makarov, Sergei Pasynkov, Yegor Bugayenko
arXiv ID
2403.15021
Category
cs.SE: Software Engineering
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
In traditional management, tasks are typically assigned to individuals, with each worker taking full responsibility for the success or failure of a task. In contrast, modern Agile, Lean, and eXtreme Programming practices advocate for shared responsibility, where an entire group is accountable for the outcome of a project or task. Despite numerous studies in other domains, the preferences of programmers have not been thoroughly analyzed. To address this gap, we conducted a survey featuring seven situational questions and collected the opinions of 120 software development practitioners. Our findings reveal that programmers prefer tasks to be assigned to them on an individual basis and appreciate taking personal responsibility for failures, as well as receiving individual rewards for successes. Understanding these preferences is crucial for project managers aiming to optimize team dynamics and ensure the successful completion of software projects.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted