The concept of software quality is very complex and has many facets. Reflecting all these facets and at the same time measuring everything related to these facets results in comprehensive but large quality models and extensive measurements. In contrast, there are also many smaller, focused quality models claiming to evaluate quality with few measures. We investigate if and to what extent it is possible to build a focused quality model with similar evaluation results as a comprehensive quality model but with far less measures needed to be collected and, hence, reduced effort. We make quality evaluations with the comprehensive Quamoco base quality model and build focused quality models based on the same set of measures and data from over 2,000 open source systems. We analyse the ability of the focused model to predict the results of the Quamoco model by comparing them with a random predictor as a baseline. We calculate the standardised accuracy measure SA and effect sizes. We found that for the Quamoco model and its 378 automatically collected measures, we can build a focused model with only 10 measures but an accuracy of 61 and a medium to high effect size. We conclude that we can build focused quality models to get an impression of a system's quality similar to comprehensive models. However, when including manually collected measures, the accuracy of the models stayed below 50 . Hence, manual measures seem to have a high impact and should therefore not be ignored in a focused model.
To the best of our knowledge there is no study directly resembling our approach of using predictors to predict a quality evaluation result from a comprehensive quality model. The approach of Nagappan and Ball @cite_14 comes closest to our approach, because they use the rules of static code checkers as independent variables, like we do. However, they again try to predict faulty components based on it with quite successful results. We take this result as an indicator that rule-based static code analysis is principally suited for quality analysis.
@cite_14
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We propose a practical defect prediction approach for companies that do not track defect related data. Specifically, we investigate the applicability of cross-company (CC) data for building localized defect predictors using static code features. Firstly, we analyze the conditions, where CC data can be used as is. These conditions turn out to be quite few. Then we apply principles of analogy-based learning (i.e. nearest neighbor (NN) filtering) to CC data, in order to fine tune these models for localization. We compare the performance of these models with that of defect predictors learned from within-company (WC) data. As expected, we observe that defect predictors learned from WC data outperform the ones learned from CC data. However, our analyses also yield defect predictors learned from NN-filtered CC data, with performance close to, but still not better than, WC data. Therefore, we perform a final analysis for determining the minimum number of local defect reports in order to learn WC defect predictors. We demonstrate in this paper that the minimum number of data samples required to build effective defect predictors can be quite small and can be collected quickly within a few months. Hence, for companies with no local defect data, we recommend a two-phase approach that allows them to employ the defect prediction process instantaneously. In phase one, companies should use NN-filtered CC data to initiate the defect prediction process and simultaneously start collecting WC (local) data. Once enough WC data is collected (i.e. after a few months), organizations should switch to phase two and use predictors learned from WC data. Background: The accurate prediction of where faults are likely to occur in code can help direct test effort, reduce costs, and improve the quality of software. Objective: We investigate how the context of models, the independent variables used, and the modeling techniques applied influence the performance of fault prediction models. Method: We used a systematic literature review to identify 208 fault prediction studies published from January 2000 to December 2010. We synthesize the quantitative and qualitative results of 36 studies which report sufficient contextual and methodological information according to the criteria we develop and apply. Results: The models that perform well tend to be based on simple modeling techniques such as Naive Bayes or Logistic Regression. Combinations of independent variables have been used by models that perform well. Feature selection has been applied to these combinations when models are performing particularly well. Conclusion: The methodology used to build models seems to be influential to predictive performance. Although there are a set of fault prediction studies in which confidence is possible, more studies are needed that use a reliable methodology and which report their context, methodology, and performance comprehensively. The quality of source code is a key factor for any software product and its continuous monitoring is an indispensable task for a software development project. We have developed a method for systematic assessing and improving the code quality of ongoing projects by using the results of various static code analysis tools. With different approaches for monitoring the quality (a trend-based one and a benchmarking-based one) and an according tool support we are able to manage the large amount of data that is generated by these static analyses. First experiences when applying the method with software projects in practice have shown the feasibility of our method. Existing defect prediction models use product or process metrics and machine learning methods to identify defect-prone source code entities. Different classifiers (e.g., linear regression, logistic regression, or classification trees) have been investigated in the last decade. The results achieved so far are sometimes contrasting and do not show a clear winner. In this paper we present an empirical study aiming at statistically analyzing the equivalence of different defect predictors. We also propose a combined approach, coined as CODEP (COmbined DEfect Predictor), that employs the classification provided by different machine learning techniques to improve the detection of defect-prone entities. The study was conducted on 10 open source software systems and in the context of cross-project defect prediction, that represents one of the main challenges in the defect prediction field. The statistical analysis of the results indicates that the investigated classifiers are not equivalent and they can complement each other. This is also confirmed by the superior prediction accuracy achieved by CODEP when compared to stand-alone defect predictors.