A Systematic Review on Software Robustness Assessment

Laranjeiro, Nuno; Agnelo, João; Bernardino, Jorge

doi:10.1145/3448977

Cited by 10 publications

(3 citation statements)

References 196 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the Prisma checklist abovementioned is not -strictly speaking- a methodological framework; rather a series of suggestions or -better- recommendations to be implemented for the sound development of any SLR (even beyond computer science), we decided to integrate it and complement it with a more specific methodological framework; the one recently developed by Kitchenham and Charters [ 39 ]. This framework was chosen due to its focus on software engineering and because its effectiveness has been amply demonstrated in previous studies [ 40 – 42 ]. We believe that complementing the general indications or recommendations outlined in the PRISMA checklist (which are valid for any field) with a framework specifically designed for research on software engineering is highly beneficial for this study, as it guarantees better accuracy.…”

Section: Slr Protocol Developmentmentioning

confidence: 99%

Prioritizing tasks in software development: A systematic literature review

Bugayenko¹,

Bakare

Cheverda

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Task prioritization is one of the most researched areas in software development. Given the huge number of papers written on the topic, it might be challenging for IT practitioners–software developers, and IT project managers–to find the most appropriate tools or methods developed to date to deal with this important issue. The main goal of this work is therefore to review the current state of research and practice on task prioritization in the Software Engineering domain and to individuate the most effective ranking tools and techniques used in the industry. For this purpose, we conducted a systematic literature review guided and inspired by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, otherwise known as the PRISMA statement. Based on our analysis, we can make a number of important observations for the field. Firstly, we found that most of the task prioritization approaches developed to date involve a specific type of prioritization strategy—bug prioritization. Secondly, the most recent works we review investigate task prioritization in terms of “pull request prioritization” and “issue prioritization,” (and we speculate that the number of such works will significantly increase due to the explosion of version control and issue management software systems). Thirdly, we remark that the most frequently used metrics for measuring the quality of a prioritization model are f-score, precision, recall, and accuracy.

show abstract

Section: Slr Protocol Developmentmentioning

confidence: 99%

Prioritizing tasks in software development: A systematic literature review

Bugayenko¹,

Bakare

Cheverda

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…The metrics were obtained by analyzing the project's source code using well-established tools. Laranjeiro et al 28 discussed state of the art in software robustness assessment, emphasizing crucial aspects like types of systems being evaluated, assessment techniques used, the target of the techniques, the types of faults used, and how system behavior is classified. The authors in ref.…”

Section: Security Vulnerabilities and Software Reliabilitymentioning

confidence: 99%

An empirical analysis for software robustness vulnerability in terms of modularity quality

Abadeh

Mirzaie

2023

Systems Engineering

View full text Add to dashboard Cite

Modern software architectures such as microservices provide a high degree of scalability, changeability, and maintainability in application development. Furthermore, enabling controlled failure of microservices can provide abstract‐level solutions to design more resilient applications. In this paper, we introduce modularity vulnerability to analyze the vulnerability of a modular software design model under the failure of m top‐rank modules by the proposed structural metrics. The study analyzes the modularity quality coefficient (MQC) under the failure of the critical modules identified using the proposed parameter‐based greedy strategy. We conduct a comprehensive analysis of the software design generated by well‐known models and online datasets and provide a perspective for reasoning about the correlation between modularity metrics. The results show that the failure of the modules with the highest cluster factor (CF) value leads to a maximum decrease in the software modularity quality. Finally, we show a linear correlation between CF and the variations of the MQC, implying stability in the software modularity analysis (SMA) problem.

show abstract

“…-Function mocks (i.e., functions used in the tests that replace the actual implementation of functions in other parts of the program being tested) should validate the parameters received against expected values, as using these mocks can hide potential problems upon that function call. -If a function returns an object, unit tests should validate, to the extent that is possible, if all the expected fields are present, as unexpected null values are known to be common sources of failures in software (Laranjeiro et al 2021). Also, unit tests should verify if the individual values present in a returning object are the expected.…”

Section: Main Findingsmentioning

confidence: 99%

Injecting software faults in Python applications

2021

Self Cite

View full text Add to dashboard Cite

Software fault injection techniques have been largely used as means for evaluating the dependability of systems in presence of certain types of faults. Despite the large diversity of tools offering the possibility of emulating the presence of software faults, there is little practical support for emulating the presence of software faults in Python applications, which are increasingly being used to support business critical cloud services. In this paper, we present FIT4Python, a tool for injecting software faults in Python code and then use it, in a mutation testing campaign, to analyse the effectiveness of OpenStack's test suite against new probable software faults. We begin by analysing the types of faults affecting Nova Compute, the core component of OpenStack. We use our tool to emulate the presence of new faults in Nova Compute API to understand how well OpenStack's battery of unit, functional, and integration tests cover these new, but probable, situations. Results show clear limitations in the effectiveness of OpenStack developers' test suites, with many cases of injected faults passing undetected through all three types of tests and that nearly half of the analysed problems could be detected with trivial changes or additions to the unit tests.

show abstract

A Systematic Review on Software Robustness Assessment

Cited by 10 publications

References 196 publications

Prioritizing tasks in software development: A systematic literature review

Prioritizing tasks in software development: A systematic literature review

An empirical analysis for software robustness vulnerability in terms of modularity quality

Injecting software faults in Python applications

Contact Info

Product

Resources

About