Predicting software development errors using software complexity metrics

Khoshgoftaar, Taghi M.; Munson, John C.

doi:10.1109/49.46879

Cited by 195 publications

(68 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a similar study, Agresti and Evanco [9] presented a model to predict defect density based on the product and process characteristics for Ada program. Moreover, there are many papers advocating statistical models and software metrics [10,11]. Most of them are based on size and complexity metrics.…”

Section: Related Workmentioning

confidence: 99%

Fault Prediction Model by Fuzzy Profile Development of Reliability Relevant Software Metrics

Pandey¹,

Goyal²

2010

IJCA

View full text Add to dashboard Cite

This paper presents a fault prediction model using reliability relevant software metrics and fuzzy inference system. For this a new approach is discussed to develop fuzzy profile of software metrics which are more relevant for software fault prediction. The proposed model predicts the fault density at the end of each phase of software development using relevant software metrics. On the basis of fault density at the end of testing phase, total number of faults in the software is predicted. The model seems to useful for both software engineer as well as project manager to optimally allocate resources and achieve more reliable software within the time and cost constraints. To validate the prediction accuracy, the model results are validated using PROMISE Software Engineering Repository Data set.

show abstract

Section: Related Workmentioning

confidence: 99%

Fault Prediction Model by Fuzzy Profile Development of Reliability Relevant Software Metrics

Pandey¹,

Goyal²

2010

IJCA

View full text Add to dashboard Cite

show abstract

“…A prediction scheme is said proactive if it predicts FP software modules merely based on some static characteristics of software code itself. For example, program code complexity [5], code size [15], [16], object-oriented metrics [8], and revision history [11] are the metrics most commonly studied. The faulty patterns are also studied by Mizuno and Kikuno [13] to discriminate FP modules and non-fault-prone (NFP) modules using a spam filtering tool.…”

Section: Introductionmentioning

confidence: 99%

Mining Co-location Relationships among Bug Reports to Localize Fault-Prone Modules

Chen

Yang

2010

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYAutomated bug localization is an important issue in software engineering. In the last few decades, various proactive and reactive localization approaches have been proposed to predict the fault-prone software modules. However, most proactive or reactive approaches need source code information or software complexity metrics to perform localization. In this paper, we propose a reactive approach which considers only bug report information and historical revision logs. In our approach, the colocation relationships among bug reports are explored to improve the prediction accuracy of a state-of-the-art learning method. Studies on three open source projects reveal that the proposed scheme can consistently improve the prediction accuracy in all three software projects by nearly 11.6% on average.

show abstract

“…There are two categories for estimating software faults using software metrics, using statistical techniques and using machine learning. The models using statistical classification techniques include Discriminant Analysis [2] and Factor Analysis [3] and those using machine learning classification techniques include decision trees [4], artificial neural networks [5], support vector machines [6] [7], etc. Many of these studies have dealt with finding the subsets of the software metrics that are most likely to predict the existence of faults.…”

Section: Introductionmentioning

confidence: 99%