Characterization of Runaway Software Projects Using Association Rule Mining

Amasaki, Sousuke; Hamano, Yasuhiro; Mizuno, Osamu; Kikuno, Tohru

doi:10.1007/11767718_35

Cited by 5 publications

(6 citation statements)

References 4 publications

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“…Amasaki et al [9] also applied the OMRON dataset to build a predictive model. Eleven rules were extracted by the association rule, and their minimum confidence was between 0.63 and 0.91.…”

Section: Related Workmentioning

confidence: 99%

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Discriminating risky software project using neural networks

Han¹

2015

Computer Standards & Interfaces

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“…Amasaki et al [9] also applied the OMRON dataset to build a predictive model. Eleven rules were extracted by the association rule, and their minimum confidence was between 0.63 and 0.91.…”

Section: Related Workmentioning

confidence: 99%

“…Examples include logistic regression [6], Bayesian classification [8], and the association rule [9]. Although the overall classification accuracy of these approaches is at an acceptable level, correctly identifying a risky project at a true-positive rate is still a challenge.…”

Section: Introductionmentioning

confidence: 99%

Discriminating risky software project using neural networks

Han¹

2015

Computer Standards & Interfaces

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“…A number of case studies have reported association-analysis methods for software engineering repositories. Amasaki et al [2] evaluated risk items for each development phase based on questionnaires to project managers, and conducted an association analysis to reveal conditions leading to project overrun (excess development budgets or delivery slippage). Their analysis target dataset, however, did not contain any quantitative variables, and rules were mined within the scope of conventional association analysis.…”

Section: Related Researchmentioning

confidence: 99%

“…So far, association rule mining has been used to discover rules hidden amongst software engineering data. For example, Amasaki et al [2] mined preconditions (combinations of risk assessment values) for software projects to fall into disorder using a dataset consisting of a large number of risk assessment variables. Song et al [8] identified rules related to defect association (types of defects occur with others).…”

Section: Introductionmentioning

confidence: 99%

Defect Data Analysis Based on Extended Association Rule Mining

Morisaki

Monden

Matsumura

et al. 2007

Fourth International Workshop on Mining Software Repositories (MSR'07:ICSE Workshops 2007)

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This paper describes an empirical study to reveal rules associated with defect correction effort. We defined defect correction effort as a quantitative (ratio scale) variable, and extended conventional (nominal scale based) association rule mining to directly handle such quantitative variables. An extended rule describes the statistical characteristic of a ratio or interval scale variable in the consequent part of the rule by its mean value and standard deviation so that conditions producing distinctive statistics can be discovered. As an analysis target, we collected various attributes of about 1,200 defects found in a typical medium-scale, multi-vendor (distance development) information system development project in Japan. Our findings based on extracted rules include: (1)Defects detected in coding/unit testing were easily corrected (less than 7% of mean effort) when they are related to data output or validation of input data. (2)Nevertheless, they sometimes required much more effort (lift of standard deviation was 5.845) in case of low reproducibility, (3)Defects introduced in coding/unit testing often required large correction effort (mean was 12.596 staff-hours and standard deviation was 25.716) when they were related to data handing. From these findings, we confirmed that we need to pay attention to types of defects having large mean effort as well as those having large standard deviation of effort since such defects sometimes cause excess effort.

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“…Researchers have used association analysis 1) effectively in the past to analyze point-of-sales (POS) data for retailers and Website traffic logs, to discover association rules hidden amongst the data 16) . There has also been research on software project data: through association analysis, Amasaki, et al 2) † Graduate School of Information Science, Nara Institute of Science and Technology mined preconditions (combination of risk assesment values) for software projects to fall into disorder using a data set consisting of a large numbers of risk assessment variables. General association analysis methods and rules, however, are not always applicable to software project data sets because they cannot directly handle quantitative (ratio scale or interval scale) variables.…”

Section: Introductionmentioning

confidence: 99%

Mining Quantitative Rules in a Software Project Data Set

Morisaki

Monden

Tamada

et al. 2007

ipsjdc

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This paper proposes a method to mine rules from a software project data set that contains a number of quantitative attributes such as staff months and SLOC. The proposed method extends conventional association analysis methods to treat quantitative variables in two ways:(1) the distribution of a given quantitative variable is described in the consequent part of a rule by its mean value and standard deviation so that conditions producing the distinctive distributions can be discovered. To discover optimized conditions, (2) quantitative values appearing in the antecedent part of a rule are divided into contiguous fine-grained partitions in preprocessing, then rules are merged after mining so that adjacent partitions are combined. The paper also describes a case study using the proposed method on a software project data set collected by Nihon Unisys Ltd. In this case, the method mined rules that can be used for better planning and estimation of the integration and system testing phases, along with criteria or standards that help with planning of outsourcing resources.

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Characterization of Runaway Software Projects Using Association Rule Mining

Cited by 5 publications

References 4 publications

Discriminating risky software project using neural networks

Discriminating risky software project using neural networks

Defect Data Analysis Based on Extended Association Rule Mining

Mining Quantitative Rules in a Software Project Data Set

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