Learning a Classifier for Prediction of Maintainability Based on Static Analysis Tools

Schnappinger, Markus; Osman, Mohd Hafeez; Pretschner, Alexander; Fietzke, Arnaud

doi:10.1109/icpc.2019.00043

Cited by 15 publications

(5 citation statements)

References 19 publications

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“…To expose the indirect effects on the methods that were not edited, this study generates the methods not edited but whose reliance on the results of the edited methods makes them impacted by the changes. This ensures that, after the changes are integrated in whole system, all the possible operational anomalous behavior will be unearthed especially where the changes made in the modified components had adverse effects on the unchanged components or where the changed code sections are utilized in the unchanged components [10].…”

Section: Resultsmentioning

confidence: 99%

“…Third, code review that involves a manual inspection of the source code by a team of expert programmers to unearth logic errors, syntax flaws, and security vulnerabilities among others [8], [9]. Fourth, code metrics analysis measuring various characteristics of the code such as complexity, coupling, and cohesion [10], [11]. The fifth approach proposed in previous research is pattern-matching techniques that examines the code to identify issues like violation to coding standards and best-practices, that have the potential of introducing defects in the code [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

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Method level static source code analysis on behavioral change impact analysis in software regression testing

Muthengi,

Mugo,

Mutua

et al. 2024

IJEECS

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Though a myriad of changes take place in a software system during maintenance, behavioral changes carry the bulk of the reasons of software modifications. In assessing the impact of the changes made in software, static source code analysis plays a key role. However, static source code analysis can be a little complex depending on the reason for the expedition. Despite the work done so far, little focus has been made on the potential of changed methods analysis during static source code analysis in assessing the impact of the changes made in a software system. We propose and investigate a static source code analysis technique that would generate information on the modified methods in the source code. This study analyzes four aThough a myriad of changes take place in a software system during maintenance, behavioral changes carry the bulk of the reasons for software modifications. In assessing the impact of the changes made in the software, static source code analysis can be a little complex depending on the reason for the expedition. Despite the works done so far, little focus has been directed on the potential of changed methods during static source code analysis, in assessing the impact of the changes made in software. This study investigates a method-level static source code analysis technique that would generate information on the methods affected by changes made in the software. The work analyzed three Java projects. The results indicate an improvement in leveraging on the knowledge of edited methods in change impact assessment during regression testing. The approach enhances code review efforts in light of assessing operational behavior impacted by the changes made.Java projects and shows that an analysis of the changed methods reveals the level of regression testing that ought to be conducted for the changes made.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Method level static source code analysis on behavioral change impact analysis in software regression testing

Muthengi,

Mugo,

Mutua

et al. 2024

IJEECS

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show abstract

“…In [21], the author presented a study using several classifiers to evaluate maintainability at the class level using the output of different static analysis tools. In their approach, ConQAT, Teamscale, and Sonarqube are used to extract metrics such as SLOC, average method length, clone coverage, etc.…”

Section: Software Maintainability Measurementmentioning

confidence: 99%

A Large Empirical Study on Automatically Classifying Software Maintainability Concerns from Issue Summaries

Chen¹,

Shoga²

2021

Adv. sci. technol. eng. syst. j.

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Software maintenance contributes the majority of software system life cycle costs. However, existing approaches with automated code analysis are limited by accuracy and scope. Using human-assessed methods or implementing quality standards are more comprehensive alternatives, but they are much more costly for smaller organizations, especially in opensource software projects. Instead, bugs are generally used to assess software quality, such as using bug fixing time as an estimate of maintenance effort. Although associated bug reports contain useful information that describe software faults, the content of these bug reports are rarely used. In this paper, we incorporate quality standards with natural language processing techniques to provide insight into software maintainability using the content of bug reports and feature requests. These issues are classified with an automated approach into various maintainability concerns whose generalizability has been validated against over 6000 issue summaries extracted from nine open source projects in previous works. Using this approach, we perform a large empirical study of 229,329 issue summaries from 61 different projects. We evaluate the differences in expressed maintainability concerns between domains, ecosystems, and types of issues. We have found differences in relative proportions across ecosystem, domain and issue severity. Further, we evaluate the evolution of maintainability across several versions in a case study of Apache Tomcat, identifying some trends within different versions and over time. In summary, our contributions include a refinement of definitions from the original empirical study on maintainability related issues, an automated approach and associated rules for identifying maintainability related quality concerns, identification of trends in the characteristics of maintainability related issue summaries through a large-scale empirical study across two major open source ecosystems, and a case study on changes in maintainability over versions in Apache Tomcat.

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“…At first, using metrics requires setting the appropriate thresholds and/or defining the acceptable intervals. Given that this is a multi-faceted problem that requires taking into account various parameters, this process is usually taken by quality experts who are responsible for examining the source code and come up with the necessary quality targets [20,21]. However, the manual examination of the source code is both time-and resources-consuming, especially for large and complex projects.…”

Section: Background Knowledgementioning

confidence: 99%

Data-Driven Analytics towards Software Sustainability: The Case of Open-Source Multimedia Tools on Cultural Storytelling

Papamichail

Symeonidis

2021

Sustainability

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The continuous evolution of modern software technologies combined with the deluge of available “ready-to-use” data has triggered revolutionary breakthroughs in several domains, preservation of cultural heritage included. This breakthrough is more than obvious just by considering the numerous multimedia tools and frameworks that actually serve as a means of providing enhanced cultural storytelling experiences (e.g., navigation in historical sites using VR, 3D modeling of artifacts, or even holograms), which are now readily available. In this context and inspired by the vital importance of sustainability as a concept that expresses the need to create the necessary conditions for future generations to use and evolve present artifacts, we target the software engineering domain and propose a systematic way towards measuring the extent to which a software artifact developed and applied in the cultural heritage domain is sustainable. To that end, we present a data-driven methodology that harnesses data residing in online software repositories and involves the analysis of various open-source multimedia tools and frameworks.

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Learning a Classifier for Prediction of Maintainability Based on Static Analysis Tools

Cited by 15 publications

References 19 publications

Method level static source code analysis on behavioral change impact analysis in software regression testing

Method level static source code analysis on behavioral change impact analysis in software regression testing

A Large Empirical Study on Automatically Classifying Software Maintainability Concerns from Issue Summaries

Data-Driven Analytics towards Software Sustainability: The Case of Open-Source Multimedia Tools on Cultural Storytelling

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