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.

EA (Enterprise Architecture) visualization methodologies have been explored by researchers and engineers to conduct EA modeling. The objectives of EA modeling are to clarify enterprise strategies, visualize business processes, and model information systems to manage resources, improve organization structure, adjust information strategy, and create new business value. Therefore, EA models can be broadly applied in various fields. For example, the applications include business modeling, information system architecture design, technology infrastructure configuration, software maintenance, and system security analysis. As the primary source of information, EA models are of paramount importance to researchers, architects, and developers. However, up to now, the purpose and means of these EA visualization methods have never been systematically analyzed and discussed, and a generalized EA visualization methodology with the ability to meet different demands is needed. The paper narrows this gap by conducting a systematic literature review on enterprise architecture visualization methodologies. In this study, 112 papers are retrieved by a manual search in 5 academic databases, a systematic literature review on EA visualization is explained to show a systematized category of visualization approaches, and then a general visualization approach is proposed by systematically reviewing the papers. Finally, the paper is concluded by discussing the contributions and limitations of the study.

Abstract-Background: Association rules are more comprehensive and understandable than fault-prone module predictors (such as logistic regression model, random forest and support vector machine). One of the challenges is that there are usually too many similar rules to be extracted by the rule mining. Aim: This paper proposes a rule reduction technique that can eliminate complex (long) and/or similar rules without sacrificing the prediction performance as much as possible. Method: The notion of the method is to removing long and similar rules unless their confidence level as a heuristic is high enough than shorter rules. For example, it starts with selecting rules with shortest length (length=1), and then it continues through the 2nd shortest rules selection (length=2) based on the current confidence level, this process is repeated on the selection for longer rules until no rules are worth included. Result: An empirical experiment has been conducted with the Mylyn and Eclipse PDE datasets. The result of the Mylyn dataset showed the proposed method was able to reduce the number of rules from 1347 down to 13, while the delta of the prediction performance was only .015 (from .757 down to .742) in terms of the F1 prediction criteria. In the experiment with Eclipsed PDE dataset, the proposed method reduced the number of rules from 398 to 12, while the prediction performance even improved (from .426 to .441.) Conclusion: The novel technique introduced resolves the rule explosion problem in association rule mining for software proneness prediction, which is significant and provides better understanding of the causes of faulty modules.

This paper proposes a fault-prone module prediction method that combines association rule mining with logistic regression analysis. In the proposed method, we focus on three key measures of interestingness of an association rule (support, confidence and lift) to select useful rules for the prediction. If a module satisfies the premise (i.e. the condition in the antecedent part) of one of the selected rules, the module is classified by the rule as either faultprone or not. Otherwise, the module is classified by the logistic model. We experimentally evaluated the prediction performance of the proposed method with different thresholds of each rule interestingness measure (support, confidence and lift) using a module set in the Eclipse project, and compared it with three well-known fault-proneness models (logistic regression model, linear discriminant model and classification tree). The result showed that the improvement of the F1-value of the proposed method was 0.163 at maximum compared to conventional models.

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