When do Software Complexity Metrics Mean Nothing? – When Examined out of Context.

Gil, Joseph A.; Lalouche, Gal

doi:10.5381/jot.2016.15.5.a2

Cited by 13 publications

(14 citation statements)

References 18 publications

(26 reference statements)

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“…Researchers have shown that metric value distributions are sensitive to context information, such as the project, the application domain, and age [50,128]. As an additional result of our study, we now know that metric value distributions are influenced by the system architecture, and there are clear reasons for these differences.…”

Section: Research Implicationssupporting

confidence: 56%

“…Zhang et al [128] showed that metric values can be affected by factors such as programming language, age, and lifespan. Recently, Gil and Lalouche [50] argued that metric values vary among projects, and "mean nothing when examined out of their context". Bouwers et al [19] also warned the community about using a metric without a proper interpretation: the "metric in a bubble" pitfall, as they call it,.…”

Section: Motivational Examplementioning

confidence: 99%

“…Indeed, the evaluation of the context has been an important discussion in the code metrics field, as other authors such as Gil and Lalouche [50] and Zhang et al [128] have shown that metric values are sensitive to contextual variables, such as programming language, age, and lifespan of a project.…”

Section: Code Metricsmentioning

confidence: 99%

“…When dealing with code metrics and code quality assessment, should software developers treat each class differently? After all, researchers have already shown that context is important to code metrics [50,128]. Suppose a class that plays the Controller role in the system: are Controllers more coupled than other classes?…”

Section: Introductionmentioning

confidence: 99%

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Context-based code quality assessment

Aniche¹

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Context-Based Code Quality Assessment Two tasks that software engineers constantly perform are writing code that is easy to evolve and maintain, and detecting poorly written pieces of code. For the former, software engineers commonly rely on well-known software architecture styles, such as Model-View-Controller (MVC). To the latter, they rely on code metrics and code smell detection approaches. However, up to now, these code metrics and code smell approaches do not take into account underlying architecture-all classes are assessed as if they were the same. In practice, software developers know that classes differ in terms of responsibilities and implementation, and thus, we expect these classes to present different levels of coupling, cohesion, and complexity. As an example, in an MVC system, Controllers are responsible for the flow between the Model and the View, and Models are responsible for representing the system's business concepts. Thus, in this thesis, we evaluate the impact of architectural roles within a system architecture on code metrics and code smells. We performed an empirical analysis in 120 open source systems, and interviewed and surveyed more than 50 software developers. Our findings show that each architectural role has a different code metric values distribution, which is a likely consequence of their specific responsibilities. Thus, we propose SATT, an approach that provides specific thresholds for architectural roles that are significantly different from others in terms of code smells. We also show that classes that play a specific architectural role contain specific code smells, which developers perceive as problems, and can impact class' change-and defect-proneness. Based on our findings, we suggest that developers understand the responsibilities of each architectural role in their system architecture, so that code metrics and code smells techniques can provide more accurate feedback.

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Section: Research Implicationssupporting

confidence: 56%

Section: Motivational Examplementioning

confidence: 99%

Section: Code Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Context-based code quality assessment

Aniche¹

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“…• Many authors have asserted that smell detection is a subjective process [73,96,84]. As Gil et al [44] say -"Bluntly, the code metric values, when inspected out of context, mean nothing." Similarly, Fontana et al [35] list a set of commonly detected smells that solve a specific design problem in the real-world.…”

Section: Rq5: What Are the Open Research Questions?mentioning

confidence: 99%

A survey on software smells

Sharma

Spinellis

2018

Journal of Systems and Software

200

146

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Context: Smells in software systems impair software quality and make them hard to maintain and evolve. The software engineering community has explored various dimensions concerning smells and produced extensive research related to smells. The plethora of information poses challenges to the community to comprehend the state-of-the-art tools and techniques. Objective: We aim to present the current knowledge related to software smells and identify challenges as well as opportunities in the current practices. Method: We explore the definitions of smells, their causes as well as effects, and their detection mechanisms presented in the current literature. We studied 445 primary studies in detail, synthesized the information, and documented our observations. Results: The study reveals five possible defining characteristics of smellsindicator, poor solution, violates best-practices, impacts quality, and recurrence. We curate ten common factors that cause smells to occur including lack of skill or awareness and priority to features over quality. We classify existing smell detection methods into five groups-metrics, rules/heuristics, history, machine learning, and optimization-based detection. Challenges in the smells detection include the tools' proneness to false-positives and poor coverage of smells detectable by existing tools.

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Will you come back to contribute? Investigating the inactivity of OSS core developers in GitHub

et al. 2022

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Several Open-Source Software (OSS) projects depend on the continuity of their development communities to remain sustainable. Understanding how developers become inactive or why they take breaks can help communities prevent abandonment and incentivize developers to come back. In this paper, we propose a novel method to identify developers’ inactive periods by analyzing the individual rhythm of contributions to the projects. Using this method, we quantitatively analyze the inactivity of core developers in 18 OSS organizations hosted on GitHub. We also survey core developers to receive their feedback about the identified breaks and transitions. Our results show that our method was effective for identifying developers’ breaks. About 94% of the surveyed core developers agreed with our state model of inactivity; 71% and 79% of them acknowledged their breaks and state transition, respectively. We also show that all core developers take breaks (at least once) and about a half of them (~45%) have completely disengaged from a project for at least one year. We also analyzed the probability of transitions to/from inactivity and found that developers who pause their activity have a ~35 to ~55% chance to return to an active state; yet, if the break lasts for a year or longer, then the probability of resuming activities drops to ~21–26%, with a ~54% chance of complete disengagement. These results may support the creation of policies and mechanisms to make OSS community managers aware of breaks and potential project abandonment.

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When do Software Complexity Metrics Mean Nothing? – When Examined out of Context.

Cited by 13 publications

References 18 publications

Context-based code quality assessment

Context-based code quality assessment

A survey on software smells

Will you come back to contribute? Investigating the inactivity of OSS core developers in GitHub

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