Improving software testing via ODC: Three case studies

Butcher, Martyn; Munro, Hugh; Kratschmer, T.

doi:10.1147/sj.411.0031

Cited by 55 publications

(34 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In order to keep the classification simple and the overhead added minimal, only the first phase is retained as part of the proposed framework. When a defect is detected, it needs to be classified according to three attributes [33]:…”

Section: Proceedings Of the Fedcsis Prague 2017mentioning

confidence: 99%

Measuring dimensions of Software Engineering projects' success in Academic context

Włodarski

Poniszewska-Marańda

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

View full text Add to dashboard Cite

Abstract-The notion of success is unsubstantial, complex and domain-specific. Software companies have been exploring its different aspects and aiming to put forward measures to capture and evaluate them. In this paper three main dimensions of success have been elicited based on previous industrial studies: project quality, project efficiency along with social factors and stakeholder's satisfaction. By investigation of their assessment criteria in commercial context a set of metrics and measures was determined and adapted to provide a structured evaluation approach for projects developed in academic setting. Professionalizing teaching and assessment process is an attempt to close a gap between workforce's expectations towards new graduates and the outcomes of their university education.

show abstract

Section: Proceedings Of the Fedcsis Prague 2017mentioning

confidence: 99%

Measuring dimensions of Software Engineering projects' success in Academic context

Włodarski

Poniszewska-Marańda

2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

“…Orthogonal Defect Classification [18] identifies weaknesses in programs throughout the system that contain faults and remove them [19][20][21][22]. The past few years, types of attack of this classification have risen tremendously, and much effort is going into trying to find a fix.…”

Section: Related Workmentioning

confidence: 99%

Cybersecurity Snapshot: Google, Twitter, and Other Online Databases

Rawal

Eberhardt

Lee

2016

JACST

View full text Add to dashboard Cite

Every day, millions of attacks are carried out on the networks and computer systems. In recent years, these numbers have increased dramatically. All it requires is one success for a hacker to gain unauthorized access and data, but for administrators, it is a constant battle to protect what is rightfully theirs. In this paper, we look into how these attacks have increased, what the studies of various databases and reports say on how and what types of data are being breached, who is breaching them, and how they are breaching the systems. Also, we propose various unconventional ways to prevent these attacks from happening in the future. Furthermore, this paper lists the top 26 bugfix times reported in the Google Security Research Project (GSRP). This article brings to light reoccurring cyber threats, challenges associated with these threats, and emerging trends in the domain of cyber security.

show abstract

“…In a controlled experiment with students, Falessi et al [18] also reported that there is affinity between some ODC defect types and previous training is needed to apply it. Nevertheless, IBM and other organizations have applied ODC to improve software development processes [19,20,21,22].…”

Section: Introductionmentioning

confidence: 99%

Learning to classify software defects from crowds: A novel approach

Hernández-González

Rodríguez

Inza

et al. 2018

Applied Soft Computing

View full text Add to dashboard Cite

In software engineering, associating each reported defect with a category allows, among many other things, for the appropriate allocation of resources. Although this classification task can be automated using standard machine learning techniques, the categorization of defects for model training requires expert knowledge, which is not always available. To circumvent this dependency, we propose to apply the learning from crowds paradigm, where training categories are obtained from multiple non-expert annotators (and so may be incomplete, noisy or erroneous) and, dealing with this subjective class information, classifiers are efficiently learnt. To illustrate our proposal, we present two real applications of the IBM's orthogonal defect classification working on the issue tracking systems from two different real domains. Bayesian network classifiers learnt using two state-of-the-art methodologies from data labeled by a crowd of annotators are used to predict the category (impact) of reported software defects. The considered methodologies show enhanced performance regarding the straightforward solution (majority voting) according to different metrics. This shows the possibilities of using non-expert knowledge aggregation techniques when expert knowledge is unavailable.

show abstract

Improving software testing via ODC: Three case studies

Cited by 55 publications

References 6 publications

Measuring dimensions of Software Engineering projects' success in Academic context

Measuring dimensions of Software Engineering projects' success in Academic context

Cybersecurity Snapshot: Google, Twitter, and Other Online Databases

Learning to classify software defects from crowds: A novel approach

Contact Info

Product

Resources

About