Improving Code: The (Mis) Perception of Quality Metrics

Pantiuchina, Jevgenija; Lanza, Michele; Bavota, Gabriele

doi:10.1109/icsme.2018.00017

Cited by 52 publications

(37 citation statements)

References 37 publications

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“…Particularly interesting is the case of readability: despite it was mentioned multiple times as a relevant feature by developers in RQ 1 , the statistical results are not aligned. This finding is likely due to the poor ability of current readability metrics, as well as proxy indicators of this aspect like complexity metrics, to capture the actual understandability of source code [69,70]. In other words, our findings support the claim for which novel metrics should be devised to better capture both structural and conceptual aspects of test code.…”

Section: B Analysis Of the Resultsmentioning

confidence: 62%

Pizza versus Pinsa: On the Perception and Measurability of Unit Test Code Quality

Grano

Iaco

Palomba

et al. 2020

2020 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Test cases are an essential asset to evaluate software quality. The research community has provided various alternatives to help developers assessing the quality of tests, like code or mutation coverage. Despite the effort spent so far, however, little is known on how practitioners perceive unit test code quality and whether the existing metrics reflect their perception. This paper aims at addressing this gap of knowledge. We first conduct semi-structured interviews and surveys with practitioners to establish a taxonomy of relevant factors for unit test quality and collect a dataset of tests rated by developers based on their perceived quality. Then, we devise a statistical model to measure how the metrics available in literature reflect the perceived quality of test cases. The findings of our study show that readability and maintainability are the key aspects for developers to diagnose the outcome of test cases and drive debugging activities. On the contrary, code coverage metrics are necessary but not sufficient to evaluate the capability of tests. Finally, we discover that available metrics are effective in characterizing poor-quality tests, while limited in distinguishing high-quality ones.

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Section: B Analysis Of the Resultsmentioning

confidence: 62%

Pizza versus Pinsa: On the Perception and Measurability of Unit Test Code Quality

Grano

Iaco

Palomba

et al. 2020

2020 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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“…Poor code quality can negatively impact the effectiveness of the testing process and the quality and reliability of the created IoT system. However, to maintain viewpoint consistency and because this topic has been sufficiently discussed in the literature [6,18,32,11,20], we decided not to include specific code quality metrics in the presented overview. The only exception is the high-level metric Quality of Code suggested by Chen et al [13], which we considered to fit well with the high-level framework used here.…”

Section: Discussionmentioning

confidence: 99%

“…Besides coupling and code complexity, Pantiuchina et al discuss other code quality metrics; cohesion and code readability in particular. Formulas to compute cohesion are also provided in [32]. The lack of code cohesion and coupling indicators is examined by Chaparro et al, and detailed formulas to quantify the properties are provided in their study [11].…”

Section: Related Workmentioning

confidence: 99%

Quality and Reliability Metrics for IoT Systems: A Consolidated View

Klima

Rechtberger

Bureš

et al. 2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Quality and reliability metrics play an important role in the evaluation of the state of a system during the development and testing phases, and serve as tools to optimize the testing process or to define the exit or acceptance criteria of the system. This study provides a consolidated view on the available quality and reliability metrics applicable to Internet of Things (IoT) systems, as no comprehensive study has provided such a view specific to these systems. The quality and reliability metrics categorized and discussed in this paper are divided into three categories: metrics assessing the quality of an IoT system or service, metrics for assessing the effectiveness of the testing process, and metrics that can be universally applied in both cases. In the discussion, recommendations of proper usage of discussed metrics in a testing process are then given.

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“…Pantiuchina et al [38] aim at bridging this gap by empirically investigating whether quality metrics are able to capture code quality improvement as perceived by developers. While previous studies [4], [14], [41] surveyed developers to investigate whether metrics align with their perception of code quality, they mine commits in which developers clearly state in the commit message their aim of improving one of four quality attributes: cohesion, coupling, code readability, and code complexity.…”

Section: Code Quality Metrics In Practicementioning

confidence: 99%

“…However, recently, several models for the detection of source code readability have come under question regarding the extent of their usefulness in practice. Research by Pantiuchina et al [38] has shown that more often than not, in practice, state-of-the-art code quality models are unable to capture quality improvements in the source code. In other words, in the context of incremental changes made to a pre-existing file, models are unable to capture improvements in the source code's cohesion, complexity, coupling, and readability.…”

Section: Introductionmentioning

confidence: 99%

A Model to Detect Readability Improvements in Incremental Changes

Roy

Fakhoury

Lee

et al. 2020

Proceedings of the 28th International Conference on Program Comprehension

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Identifying source code that has poor readability allows developers to focus maintenance efforts on problematic code. Therefore, the effort to develop models that can quantify the readability of a piece of source code has been an area of interest for software engineering researchers for several years. However, recent research questions the usefulness of these readability models in practice. When applying these models to readability improvements that are made in practice, i.e., commits, they are unable to capture these incremental improvements, despite a clear perceived improvement by the developers. This results in a discrepancy between the models we have built to measure readability, and the actual perception of readability in practice. In this work, we propose a model that is able to detect incremental readability improvements made by developers in practice with an average precision of 79.2% and an average recall of 67% on an unseen test set. We then investigate the metrics that our model associates with developer perceived readability improvements as well as non-readability changes. Finally, we compare our model to existing state-of-the-art readability models, which our model outperforms by at least 23% in terms of precision and 42% in terms of recall. CCS CONCEPTS • Software and its engineering → Maintaining software; • Computing methodologies → Machine learning.

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Improving Code: The (Mis) Perception of Quality Metrics

Cited by 52 publications

References 37 publications

Pizza versus Pinsa: On the Perception and Measurability of Unit Test Code Quality

Pizza versus Pinsa: On the Perception and Measurability of Unit Test Code Quality

Quality and Reliability Metrics for IoT Systems: A Consolidated View

A Model to Detect Readability Improvements in Incremental Changes

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