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Computer Science > Software Engineering

arXiv:2101.06733 (cs)
[Submitted on 17 Jan 2021]

Title:Profiling Software Developers with Process Mining and N-Gram Language Models

Authors:João Caldeira, Fernando Brito e Abreu, Jorge Cardoso, Ricardo Ribeiro, Claudia Werner
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Abstract:Context: Profiling developers is challenging since many factors, such as their skills, experience, development environment and behaviors, may influence a detailed analysis and the delivery of coherent interpretations.
Objective: We aim at profiling software developers by mining their software development process. To do so, we performed a controlled experiment where, in the realm of a Python programming contest, a group of developers had the same well-defined set of requirements specifications and a well-defined sprint schedule. Events were collected from the PyCharm IDE, and from the Mooshak automatic jury where subjects checked-in their code.
Method: We used n-gram language models and text mining to characterize developers' profiles, and process mining algorithms to discover their overall workflows and extract the correspondent metrics for further evaluation.
Results: Findings show that we can clearly characterize with a coherent rationale most developers, and distinguish the top performers from the ones with more challenging behaviors. This approach may lead ultimately to the creation of a catalog of software development process smells.
Conclusions: The profile of a developer provides a software project manager a clue for the selection of appropriate tasks he/she should be assigned. With the increasing usage of low and no-code platforms, where coding is automatically generated from an upper abstraction layer, mining developer's actions in the development platforms is a promising approach to early detect not only behaviors but also assess project complexity and model effort.
Subjects: Software Engineering (cs.SE)
Cite as: arXiv:2101.06733 [cs.SE]
  (or arXiv:2101.06733v1 [cs.SE] for this version)
  https://doi.org/10.48550/arXiv.2101.06733

Submission history

From: Joao Caldeira [view email]
[v1] Sun, 17 Jan 2021 18:03:14 UTC (637 KB)
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