Navigating Expertise in Configurable Software Systems through the Maze of Variability

• URL: http://arxiv.org/abs/2401.10699v1
• Date: Fri, 19 Jan 2024 14:03:33 GMT
• Title: Navigating Expertise in Configurable Software Systems through the Maze of Variability
• Authors: Karolina Milano, Bruno Cafeo
• Abstract summary: This research study investigates the distribution of development efforts in CSS. It also examines the engagement of designated experts with variable code in their assigned files.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The understanding of source code in large-scale software systems poses a challenge for developers. The role of expertise in source code becomes critical for identifying developers accountable for substantial changes. However, in the context of configurable software systems (CSS) using pre-processing and conditional compilation, conventional expertise metrics may encounter limitations due to the non-alignment of variability implementation with the natural module structure. This early research study investigates the distribution of development efforts in CSS, specifically focusing on variable and mandatory code. It also examines the engagement of designated experts with variable code in their assigned files. The findings provide insights into task allocation dynamics and raise questions about the applicability of existing metrics, laying the groundwork for alternative approaches to assess developer expertise in handling variable code. This research aims to contribute to a comprehensive understanding of challenges within CSS, marking initial steps toward advancing the evaluation of expertise in this context.

Related papers

• Understanding Code Understandability Improvements in Code Reviews [79.16476505761582]
  We analyzed 2,401 code review comments from Java open-source projects on GitHub. 83.9% of suggestions for improvement were accepted and integrated, with fewer than 1% later reverted.
  arXiv  Detail & Related papers  (2024-10-29T12:21:23Z)
• CodeMMLU: A Multi-Task Benchmark for Assessing Code Understanding Capabilities of CodeLLMs [9.649864680130781]
  We present CodeMMLU, a benchmark designed to evaluate the depth of software and code understanding in CodeLLMs. CodeMMLU includes over 10,000 questions sourced from diverse domains, encompassing tasks such as code analysis, defect detection, and software engineering principles. Our evaluation reveals that even state-of-the-art models face significant challenges with CodeMMLU.
  arXiv  Detail & Related papers  (2024-10-02T20:04:02Z)
• GEMS: Generative Expert Metric System through Iterative Prompt Priming [18.0413505095456]
  Non-experts can find it unintuitive to create effective measures or transform theories into context-specific metrics. This technical report addresses this challenge by examining software communities within large software corporations. We propose a prompt-engineering framework inspired by neural activities, demonstrating that generative models can extract and summarize theories.
  arXiv  Detail & Related papers  (2024-10-01T17:14:54Z)
• Assessing Consensus of Developers' Views on Code Readability [3.798885293742468]
  Developers now spend more time reviewing code than writing it, highlighting the importance of Code Readability for code comprehension. Previous research found that existing Code Readability models were inaccurate in representing developers' notions. We surveyed 10 Java developers with similar coding experience to evaluate their consensus on Code Readability assessments and related aspects.
  arXiv  Detail & Related papers  (2024-07-04T09:54:42Z)
• Impermanent Identifiers: Enhanced Source Code Comprehension and Refactoring [43.5512514983067]
  This article introduces an innovative approach to code augmentation centered around Impermanent Identifiers. The primary goal is to enhance the software development experience by introducing dynamic identifiers that adapt to changing contexts. This study rigorously evaluates the adoption and acceptance of Impermanent Identifiers within the software development landscape.
  arXiv  Detail & Related papers  (2024-06-13T12:54:02Z)
• On The Importance of Reasoning for Context Retrieval in Repository-Level Code Editing [82.96523584351314]
  We decouple the task of context retrieval from the other components of the repository-level code editing pipelines. We conclude that while the reasoning helps to improve the precision of the gathered context, it still lacks the ability to identify its sufficiency.
  arXiv  Detail & Related papers  (2024-06-06T19:44:17Z)
• How Far Have We Gone in Binary Code Understanding Using Large Language Models [51.527805834378974]
  We propose a benchmark to evaluate the effectiveness of Large Language Models (LLMs) in binary code understanding. Our evaluations reveal that existing LLMs can understand binary code to a certain extent, thereby improving the efficiency of binary code analysis.
  arXiv  Detail & Related papers  (2024-04-15T14:44:08Z)
• A Survey of Neural Code Intelligence: Paradigms, Advances and Beyond [84.95530356322621]
  This survey presents a systematic review of the advancements in code intelligence. It covers over 50 representative models and their variants, more than 20 categories of tasks, and an extensive coverage of over 680 related works. Building on our examination of the developmental trajectories, we further investigate the emerging synergies between code intelligence and broader machine intelligence.
  arXiv  Detail & Related papers  (2024-03-21T08:54:56Z)
• DevBench: A Comprehensive Benchmark for Software Development [72.24266814625685]
  DevBench is a benchmark that evaluates large language models (LLMs) across various stages of the software development lifecycle. Empirical studies show that current LLMs, including GPT-4-Turbo, fail to solve the challenges presented within DevBench. Our findings offer actionable insights for the future development of LLMs toward real-world programming applications.
  arXiv  Detail & Related papers  (2024-03-13T15:13:44Z)
• CoreDiag: Eliminating Redundancy in Constraint Sets [68.8204255655161]
  We present a new algorithm which can be exploited for the determination of minimal cores (minimal non-redundant constraint sets) The algorithm is especially useful for distributed knowledge engineering scenarios where the degree of redundancy can become high. In order to show the applicability of our approach, we present an empirical study conducted with commercial configuration knowledge bases.
  arXiv  Detail & Related papers  (2021-02-24T09:16:10Z)
• Representation of Developer Expertise in Open Source Software [12.583969739954526]
  We use the World of Code infrastructure to extract the complete set of APIs in the files changed by open source developers. We then employ Doc2Vec embeddings for vector representations of APIs, developers, and projects. We evaluate if these embeddings reflect the postulated topology of the Skill Space.
  arXiv  Detail & Related papers  (2020-05-20T16:36:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.