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On the relationship between source-code metrics and cognitive load: A systematic tertiary review

JOURNAL OF SYSTEMS AND SOFTWARE 2023年 198卷

作者： Abbad-Andaloussi, Amine Univ St Gallen Inst Comp Sci CH-9000 St Gallen Switzerland

The difficulty of software development tasks depends on several factors including the characteristics of the underlying source-code. These characteristics can be captured and measured using source -code metrics, which, in turn, can provide indications about the difficulty of the source-code. From a cognitive perspective, this difficulty is due to an increase in developers' cognitive load, which can be estimated using psycho-physiological measures. Based on these measures, a handful of studies investigated the relationship between source-code metrics and cognitive load. For most of the metrics, such a relationship could not be established. While these studies used a small subset of metrics, the literature comprises hundreds of other metrics. Despite the existing reviews surveying these metrics, a consolidated overview is still needed to understand their properties and leverage their potential to align with cognitive load. This need is addressed in this paper through a Systematic Tertiary Review (STR) covering the full spectrum of source-code metrics, studying their properties and investigating their potential relationship to cognitive load. The outcome of this STR is intended to guide practitioners in choosing appropriate metrics, set the grounds for conceptualizing the relationship between source-code metrics and cognitive load and raise new research challenges for the future. (c) 2023 The Author. Published by Elsevier Inc. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： source-code metrics Software quality source-code readability Cognitive load

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From source code to runtime behaviour: Software metrics help to select the computer architecture

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KNOWLEDGE-BASED SYSTEMS 2010年第4期23卷 343-349页

作者： Eichinger, Frank Kramer, David Boehm, Klemens Karl, Wolfgang KIT Inst Programme Struct & Data Org IPD D-76128 Karlsruhe Germany KIT Inst Comp Sci & Engn ITEC D-76128 Karlsruhe Germany

The decision which hardware platform to use for a certain application is an important problem in computer architecture This paper reports on a study where a data-mining approach is used for this decision It relies purely on source-code characteristics, to avoid potentially expensive programme executions. One challenge in this context is that one cannot infer how often functions that are part of the application are typically executed The main Insight of this study is twofold. (a) source-code characteristics are sufficient nevertheless (b) Linking individual functions with the runtime behaviour of the programme as a whole yields good predictions In other words, while individual data objects from the training set may be quite inaccurate, the resulting model is not (C) 2009 Elsevier B V All rights reserved

关键词： Computer architecture Data mining Performance prediction source-code metrics Control-flow graphs

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From source code to runtime behaviour: Software metrics help to select the computer architecture

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29th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence

作者： Eichinger, Frank Kramer, David Boehm, Klemens Karl, Wolfgang KIT Inst Programme Struct & Data Org IPD D-76128 Karlsruhe Germany KIT Inst Comp Sci & Engn ITEC D-76128 Karlsruhe Germany

ISBN: (纸本)9781848829824

关键词： Computer architecture Data mining Performance prediction source-code metrics Control-flow graphs

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