Adapting a fault prediction model to allow inter languagereuse

Watanabe, Shinya; Kaiya, Haruhiko; Kaijiri, Kenji

doi:10.1145/1370788.1370794

Cited by 111 publications

(79 citation statements)

References 21 publications

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“…Since the performance of CPDP is usually very poor [59], researchers have proposed various techniques to improve CPDP [29,37,51,54].…”

Section: Background and Related Workmentioning

confidence: 99%

“…The metric compensation transforms a target dataset similar to a source dataset by using the average metric values [54]. To evaluate the performance of the metric compensation, Watanabe et al collected two defect datasets with the same metric set (8 object-oriented metrics) from two software projects and then conducted CPDP [54].…”

Section: Background and Related Workmentioning

confidence: 99%

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Heterogeneous defect prediction

Nam

Kim

2015

Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering

128

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Software defect prediction is one of the most active research areas in software engineering. We can build a prediction model with defect data collected from a software project and predict defects in the same project, i.e. within-project defect prediction (WPDP). Researchers also proposed crossproject defect prediction (CPDP) to predict defects for new projects lacking in defect data by using prediction models built by other projects. In recent studies, CPDP is proved to be feasible. However, CPDP requires projects that have the same metric set, meaning the metric sets should be identical between projects. As a result, current techniques for CPDP are difficult to apply across projects with heterogeneous metric sets.To address the limitation, we propose heterogeneous defect prediction (HDP) to predict defects across projects with heterogeneous metric sets. Our HDP approach conducts metric selection and metric matching to build a prediction model between projects with heterogeneous metric sets. Our empirical study on 28 subjects shows that about 68% of predictions using our approach outperform or are comparable to WPDP with statistical significance.

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“…Since the performance of CPDP is usually very poor [59], researchers have proposed various techniques to improve CPDP [29,37,51,54].…”

Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Heterogeneous defect prediction

Nam

Kim

2015

Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering

128

View full text Add to dashboard Cite

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“…Thus, change data collection requires analysis of the software's project history. However in many cases, this data may either be unavailable or difficult to collect [1][2][3]. This limits the applicability of change prediction models for those software projects where the local data is scarce in nature.…”

Section: Introductionmentioning

confidence: 99%

“…They also investigate a new group of bio-inspired techniques better known as Artificial Immune System (AIS) algorithms for their ability to detect change prone classes using inter project validation. This paper investigates the following research questions: (1) Can the prediction model of one software project be effectively used to determine change prone classes of another, i.e. is inter project validation feasible?…”

Section: Introductionmentioning

confidence: 99%

Applicability of Inter Project Validation for Determination of Change Prone Classes

Malhotra¹,

Gupta²,

Khanna³

2014

IJCA

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The research in the field of defect and change proneness prediction of software has gained a lot of momentum over the past few years. Indeed, effective prediction models can help software practitioners in detecting the change prone modules of a software, allowing them to optimize the resources used for software testing. However, the development of the prediction models used to determine change prone classes are dependent on the availability of historical data from the concerned software. This can pose a challenge in the development of effective change prediction models. The aim of this paper is to address this limitation by using the data from models based on similar projects to predict the change prone classes of the concerned software. This inter project technique can facilitate the development of generalized models which can be used to ascertain change prone classes for multiple software projects. It would also lead to optimization of critical time and resources in the testing and maintenance phases. This work evaluates the effectiveness of statistical and machine learning techniques for developing such models using receiver operating characteristic analysis. The observations of the study indicate varied results for the different techniques used.

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“…A potential way of predicting faults for projects of these companies without historical fault data is to make use of these public and open source projects as training data. Cross-project fault prediction refers to predicting faults in a project using prediction models trained from historical data of other projects [9,10]. There are some studies focusing on this issue and their results show that cross-project fault prediction is still a serious challenge [9,11].For machine learning based predictions, the effect of a predictor depends on two factors: the training data and the learning algorithm.…”

Section: Introductionmentioning

confidence: 99%

Cross Company and within Company Fault Prediction using Object Oriented Metrics

Singh¹,

Verma²,

Vyas³

2013

IJCA

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This paper investigates fault predictions in the cross-project context focusing on the object oriented metrics for the organizations that do not track fault related data. In this study, empirical analysis is carried out to validate object-oriented Chidamber and Kemerer (CK) design metrics for cross project fault prediction. The machine learning techniques used for evaluation are J48, NB, SVM, RF, K-NN and DT. The results indicate CK metrics can be used as initial guideline for the projects where no previous fault data is available. Overall, the results of cross company is comparable to the within company data learning. Our analysis is in favour of reusability in object oriented technology and it has been empirically shown that object oriented metric data can be used for cross company fault prediction in initial stage when previous fault data of the project is not available.

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Adapting a fault prediction model to allow inter languagereuse

Cited by 111 publications

References 21 publications

Heterogeneous defect prediction

Heterogeneous defect prediction

Applicability of Inter Project Validation for Determination of Change Prone Classes

Cross Company and within Company Fault Prediction using Object Oriented Metrics

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