An Automated Approach for Software Bug Classification

Neelofar,; Javed, Muhammad Awais; Mohsin, Hufsa

doi:10.1109/cisis.2012.132

Cited by 20 publications

(6 citation statements)

References 5 publications

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“…The complexes were prepared by the method in literature [19]. Equimolar ethanolic solution of ligands (HNLH and HNSM) (0.0025 mol) and SnCl 2 .2H 2 O (0.0025 mol) were refluxed with constant stirring for 2 h (Scheme 2).…”

Section: Preparation Of Complexesmentioning

confidence: 99%

Synthesis of Schiff bases derived from 2-hydroxy-1- naphth- aldehyde and their tin(II) complexes for antimicribial and antioxidant activities

Neelofar¹,

Ali²,

Khan³

et al. 2018

Bull. Chem. Soc. Eth.

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ABSTRACT.The current studies were designed to prepare tin(II) complexes of various Schiff base derivatives of 2-hydroxy-1-naphthaldehyde (HN) with L-histidine and sulfamethazine have been prepared and characterized by different physiochemical studies such as elemental analysis, atomic absorption, UV-Vis spectra, FTIR spectra, 1 H-NMR, 13 C-NMR and conductance studies. Antimicrobial and antioxidant activities were also calculated. Antibacterial activity was evaluated by the agar-well diffusion method. Two Gram-negative (Klebsiella pneumoniae and Escherichia coli) and three Gram-positive (Staphylococcus aureus, Staphylococcus epidermidis and Bacillus subtilis) bacterial strains were used. Antifungal activity was resolute against three fungal strains (Aspergillus niger, Aspergillus flavus and Alternaria solani) by using the agar tube dilution method. The antioxidant activity of ligands and their complexes was measured on the basis of the radical scavenging effect of 1,1-diphenyl-2-picryl-hydrazyl (DPPH)-free radical activity. Ligand HNSM exhibited excellent activities as antibacterial activity (22 mm), antifungal activity (55%) and antioxidant activity (119 ppm).

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Section: Preparation Of Complexesmentioning

confidence: 99%

Synthesis of Schiff bases derived from 2-hydroxy-1- naphth- aldehyde and their tin(II) complexes for antimicribial and antioxidant activities

Neelofar¹,

Ali²,

Khan³

et al. 2018

Bull. Chem. Soc. Eth.

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show abstract

“…Feature selection and bi-grams are used and it has been observed the using the bi-gram, a little improvement in the performance can be achieved, however as the result also depend on the type of the project, so in some case use of the bi-gram may yield to bad results (Roy & Rossi, 2012). Neelofar et.al (Neelofar, Javed, & Javed, 2012) expressed that for a developer task of tracking the bug is very difficult and time consuming, as the developers are expert in a particular area. For example, some developers are expert in creating the interface for the end user and other functionality of any high level language.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiclass Software Bug Severity Classification using Decision Tree, Naive Bayes and Bagging

Kumar¹

2021

TURCOMAT

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The software applications are experiencing the challenges of ever-growing complexity caused by the increase in the number of bugs. The software development process has been adversely affected due to the wastage of resources caused due to the bugs. It is imperative to identify and predict bugs to facilitate the software development process. Software bugs can be classified according to the severity of the bugs. In this paper a comparative analysis of Decision Tree, Naïve Bayes and Bagging approach is done for the bug severity classification. A comparative analysis of the Naïve Bayes, Decision Tree and Bagging approach is done for the accuracy, precision, recall and F-measure parameters

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“…Bug classification is a possible research area that can be explored with our dataset, using all the data sources it offers Neelofar et al (2012); Pingclasai et al (2013). In applications with different components connected to the network or that make use of a database system, they can lead to classifications not previously considered.…”

Section: Applications For a Dataset Of End-to-end Bugs In Software Researchmentioning

confidence: 99%

A dataset of regressions in web applications detected by end-to-end tests

et al. 2021

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End-to-end tests present many challenges in the industry. The long-running times of these tests make it unsuitable to apply research work on test case prioritization or test case selection, for instance, on them, as most works on these two problems are based on datasets of unit tests. These ones are fast to run, and time is not usually a considered criterion. This is because there is no dataset of end-to-end tests, due to the infrastructure needs for running this kind of tests, the complexity of the setup and the lack of proper characterization of the faults and their fixes. Therefore, running end-to-end tests for any research work is hard and time-consuming, and the availability of a dataset containing regression bugs, documentation and logs for these tests might foster the usage of end-to-end tests in research works. This paper presents a) a dataset for this kind of tests, including six well-documented manually injected regression bugs and their corresponding fixes in three web applications built using Java and the Spring framework; b) tools for easing the execution of these tests no matter the infrastructure; and c) a comparative study with two well-known datasets of unit tests. The comparative study shows that there are important differences between end-to-end and unit tests, such as their execution time and the amount of resources they consume, which are much higher in the end-to-end tests. End-to-end testing deserves some attention from researchers. Our dataset is a first effort toward easing the usage of end-to-end tests in research works.

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An Automated Approach for Software Bug Classification

Cited by 20 publications

References 5 publications

Synthesis of Schiff bases derived from 2-hydroxy-1- naphth- aldehyde and their tin(II) complexes for antimicribial and antioxidant activities

Synthesis of Schiff bases derived from 2-hydroxy-1- naphth- aldehyde and their tin(II) complexes for antimicribial and antioxidant activities

Multiclass Software Bug Severity Classification using Decision Tree, Naive Bayes and Bagging

A dataset of regressions in web applications detected by end-to-end tests

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