In software engineering, relying on experience can render maintainability expertise into prejudice over time. For example, based on their own experience, some consider JavaScript as inelegant language and hence of lowest maintainability. Such prejudice should not guide decisions without prior empirical validation.
Several studies investigated aspects of software quality. Kochhar, Wijedasa and Lo @cite_24 present a study on code quality of GitHub code which examines the impact of language co-use on code quality. @cite_9 did a study on software quality of industry code which investigates the influence of distributed development on software quality. @cite_39 also present a study on software quality of industry code which studies the impact of code ownership on software quality. Nagappan, Murphy and Basili @cite_33 examine software quality of industry software by investigating the effect of organizational structure on software quality. All these studies consider different aspects of software quality than this study and operationalize quality differently.
@cite_24 @cite_9 @cite_33 @cite_39
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Software practitioners lack a consistent approach to assessing and predicting quality within their products. This research proposes a software quality model that accounts for the influences of development team skill experience, process maturity, and problem complexity throughout the software engineering life cycle. The model is structured using Bayesian Belief Networks and, unlike previous efforts, uses widely-accepted software engineering standards and in-use industry techniques to quantify the indicators and measures of software quality. Data from 28 software engineering projects was acquired for this study, and was used for validation and comparison of the presented software quality models. Three Bayesian model structures are explored and the structure with the highest performance in terms of accuracy of fit and predictive validity is reported. In addition, the Bayesian Belief Networks are compared to both Least Squares Regression and Neural Networks in order to identify the technique is best suited to modeling software product quality. The results indicate that Bayesian Belief Networks outperform both Least Squares Regression and Neural Networks in terms of producing modeled software quality variables that fit the distribution of actual software quality values, and in accurately forecasting 25 different indicators of software quality. Between the Bayesian model structures, the simplest structure, which relates software quality variables to their correlated causal factors, was found to be the most effective in modeling software quality. In addition, the results reveal that the collective skill and experience of the development team, over process maturity or problem complexity, has the most significant impact on the quality of software products. We performed an empirical study of the relation between technical quality of software products and the issue resolution performance of their maintainers. In particular, we tested the hypothesis that ratings for source code maintainability, as employed by the Software Improvement Group (SIG) quality model, are correlated with ratings for issue resolution speed. We tested the hypothesis for issues of type defect and of type enhancement. This study revealed that all but one of the metrics of the SIG quality model show a significant positive correlation with the resolution speed of defects, enhancements, or both. To study the impact of code clones on software quality, researchers typically carry out their studies based on fine-grained analysis of inconsistent changes at the revision level. As a result, they capture much of the chaotic and experimental nature inherent in any on-going software development process. Analyzing highly fluctuating and short-lived clones is likely to exaggerate the ill effects of inconsistent changes on the quality of the released software product, as perceived by the end user. To gain a broader perspective, we perform an empirical study on the effect of inconsistent changes on software quality at the release level. Based on a case study on three open source software systems, we observe that only 1.02 -4.00 of all clone genealogies introduce software defects at the release level, as opposed to the substantially higher percentages reported by previous studies at the revision level. Our findings suggest that clones do not have a significant impact on the post-release quality of the studied systems, and that the developers are able to effectively manage the evolution of cloned code. Traditionally, research on quality attributes was either kept under wraps within the organization that performed it, or carried out by outsiders using narrow, black-box techniques. The emergence of open source software has changed this picture allowing us to evaluate both software products and the processes that yield them. Thus, the software source code and the associated data stored in the version control system, the bug tracking databases, the mailing lists, and the wikis allow us to evaluate quality in a transparent way. Even better, the large number of (often competing) open source projects makes it possible to contrast the quality of comparable systems serving the same domain. Furthermore, by combining historical source code snapshots with significant events, such as bug discoveries and fixes, we can further dig into the causes and effects of problems. Here we present motivating examples, tools, and techniques that can be used to evaluate the quality of open source (and by extension also proprietary) software.