We present a study were software systems are considered as complex networks which have a self-similar structure under a length-scale transformation. On such complex software networks we computed a self-similar coefficient, also known as fractal dimension, using "the box counting method". We analyzed various releases of the publically available Eclipse software systems, calculating the fractal dimension for twenty sub-projects, randomly chosen, for every release, as well as for each release as a whole. Our results display an overall consistency among the sub-projects and among all the analyzed releases. We found a very good correlation between the fractal dimension and the number of bugs for Eclipse and for twenty sub-projects. Since the fractal dimension is just a scalar number that characterizes a whole system, while complexity and quality metrics are in general computed on every system module, this result suggests that the fractal dimension could be considered as a global quality metric for large software systems. Our results need however to be confirmed for other large software systems.
{"title":"The fractal dimension metric and its use to assess object-oriented software quality","authors":"I. Turnu, G. Concas, M. Marchesi, R. Tonelli","doi":"10.1145/1985374.1985391","DOIUrl":"https://doi.org/10.1145/1985374.1985391","url":null,"abstract":"We present a study were software systems are considered as complex networks which have a self-similar structure under a length-scale transformation. On such complex software networks we computed a self-similar coefficient, also known as fractal dimension, using \"the box counting method\".\u0000 We analyzed various releases of the publically available Eclipse software systems, calculating the fractal dimension for twenty sub-projects, randomly chosen, for every release, as well as for each release as a whole. Our results display an overall consistency among the sub-projects and among all the analyzed releases.\u0000 We found a very good correlation between the fractal dimension and the number of bugs for Eclipse and for twenty sub-projects. Since the fractal dimension is just a scalar number that characterizes a whole system, while complexity and quality metrics are in general computed on every system module, this result suggests that the fractal dimension could be considered as a global quality metric for large software systems. Our results need however to be confirmed for other large software systems.","PeriodicalId":103819,"journal":{"name":"Workshop on Emerging Trends in Software Metrics","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122956041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automated In-Process Software Engineering Measurement and Analysis (AISEMA) systems represent a major advancement in tracking non-invasively the activities of developers. We have built on the top of an AISEMA system a model that enables to understand better how the tools are used in practical real-life development settings. In this work we evaluate to what extent experienced developers change their activities during mentoring activities in Pair Programming (PP) and, in this case, how long this effect can be observed. We compare how the experienced developers use the tools when working with other experts and when with new developers. The results indicate that there is a notable difference in the way the tools are used between the experts working together and the experts mentoring the new developers that have just joined the team. Moreover, over time the difference between pairs of experts and mixed pairs (experts and novices) working together becomes almost unnoticeable.
{"title":"Analyzing tool usage to understand to what extent experts change their activities when mentoring","authors":"P. Abrahamsson, Ilenia Fronza, Jelena Vlasenko","doi":"10.1145/1985374.1985380","DOIUrl":"https://doi.org/10.1145/1985374.1985380","url":null,"abstract":"Automated In-Process Software Engineering Measurement and Analysis (AISEMA) systems represent a major advancement in tracking non-invasively the activities of developers. We have built on the top of an AISEMA system a model that enables to understand better how the tools are used in practical real-life development settings. In this work we evaluate to what extent experienced developers change their activities during mentoring activities in Pair Programming (PP) and, in this case, how long this effect can be observed. We compare how the experienced developers use the tools when working with other experts and when with new developers. The results indicate that there is a notable difference in the way the tools are used between the experts working together and the experts mentoring the new developers that have just joined the team. Moreover, over time the difference between pairs of experts and mixed pairs (experts and novices) working together becomes almost unnoticeable.","PeriodicalId":103819,"journal":{"name":"Workshop on Emerging Trends in Software Metrics","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129603978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this keynote I explore what exactly do we mean by data quality, techniques to assess data quality and the very significant challenges that poor data quality can pose. I believe we neglect data quality at our peril since - whether we like it or not - our research results are founded upon data and our assumptions that data quality issues do not confound our results. A systematic review of the literature suggests that it is a minority practice to even explicitly discuss data quality. I therefore suggest that this topic should become a higher priority amongst empirical software engineering researchers.
{"title":"Data quality: cinderella at the software metrics ball?","authors":"M. Shepperd","doi":"10.1145/1985374.1985376","DOIUrl":"https://doi.org/10.1145/1985374.1985376","url":null,"abstract":"In this keynote I explore what exactly do we mean by data quality, techniques to assess data quality and the very significant challenges that poor data quality can pose. I believe we neglect data quality at our peril since - whether we like it or not - our research results are founded upon data and our assumptions that data quality issues do not confound our results. A systematic review of the literature suggests that it is a minority practice to even explicitly discuss data quality. I therefore suggest that this topic should become a higher priority amongst empirical software engineering researchers.","PeriodicalId":103819,"journal":{"name":"Workshop on Emerging Trends in Software Metrics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131003302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To assess the quality of software, two ingredients are available today: (1) quality models defining abstract quality characteristics and (2) code analysis tools providing a large variety of metrics. However, there exists a gap between these two worlds. The quality attributes defined in quality models are too abstract to be operationalized. On the other side, the aggregation of the results of static code analysis tools remains a challenge. We address these problems by defining a quality model based on an explicit meta-model. It allows to operationalize quality models by defining how metrics calculated by tools are aggregated. Furthermore, we propose a new approach for normalizing the results of rule-based code analysis tools, which uses the information on the structure of the source code in the quality model. We evaluate the quality model by providing tool support for both developing quality models and conducting automatic quality assessments. Our results indicate that large quality models can be built based on our meta-model. The automatic assessment shows a high correlation between the automatic assessment and an expert-based ranking.
{"title":"Integrating quality models and static analysis for comprehensive quality assessment","authors":"K. Lochmann, L. Heinemann","doi":"10.1145/1985374.1985378","DOIUrl":"https://doi.org/10.1145/1985374.1985378","url":null,"abstract":"To assess the quality of software, two ingredients are available today: (1) quality models defining abstract quality characteristics and (2) code analysis tools providing a large variety of metrics. However, there exists a gap between these two worlds. The quality attributes defined in quality models are too abstract to be operationalized. On the other side, the aggregation of the results of static code analysis tools remains a challenge. We address these problems by defining a quality model based on an explicit meta-model. It allows to operationalize quality models by defining how metrics calculated by tools are aggregated. Furthermore, we propose a new approach for normalizing the results of rule-based code analysis tools, which uses the information on the structure of the source code in the quality model. We evaluate the quality model by providing tool support for both developing quality models and conducting automatic quality assessments. Our results indicate that large quality models can be built based on our meta-model. The automatic assessment shows a high correlation between the automatic assessment and an expert-based ranking.","PeriodicalId":103819,"journal":{"name":"Workshop on Emerging Trends in Software Metrics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124404596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
One technique from value-based software engineering involves prioritising the system and selectively applying time-consuming techniques (such as traceability) in order to maximise return on investment. This prioritisation could be based on predicted change-proneness of code modules, if a sufficiently accurate prediction can be achieved. Several previous studies have examined links between software change-proneness and software metrics such as size and complexity. However, conclusions differ as to the strength of the relationships. We present here a new case study project, extracting a range of complexity values from the code modules and testing for the existence of a significant link between change-proneness and complexity. We find only limited evidence of a linear relationship, but analysis using other statistical techniques does reveal some other significant links.
{"title":"Linking software design metrics to component change-proneness","authors":"C. Ingram, S. Riddle","doi":"10.1145/1985374.1985384","DOIUrl":"https://doi.org/10.1145/1985374.1985384","url":null,"abstract":"One technique from value-based software engineering involves prioritising the system and selectively applying time-consuming techniques (such as traceability) in order to maximise return on investment. This prioritisation could be based on predicted change-proneness of code modules, if a sufficiently accurate prediction can be achieved. Several previous studies have examined links between software change-proneness and software metrics such as size and complexity. However, conclusions differ as to the strength of the relationships. We present here a new case study project, extracting a range of complexity values from the code modules and testing for the existence of a significant link between change-proneness and complexity. We find only limited evidence of a linear relationship, but analysis using other statistical techniques does reveal some other significant links.","PeriodicalId":103819,"journal":{"name":"Workshop on Emerging Trends in Software Metrics","volume":"25 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122507030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Effective component testing (or commonly termed as Unit Testing) is important to control defect slippage into the testing stage. Often testing teams lack in-process visibility into the effectiveness of ongoing component testing. Using project data such as code coverage and schedule and effort estimates, we generate temporal and rate-based insights into component testing effectiveness. A simple composite metric is used for measuring and forecasting the health of the component testing process. The early warning signals, based on the forecast and associated insights, lead teams to take proactive actions for improving component testing. In our ongoing experimental studies, we have observed that use of these insights cause a substantial reduction in defect slippage.
{"title":"Insights into component testing process","authors":"Vikrant S. Kaulgud, V. Sharma","doi":"10.1145/1985374.1985382","DOIUrl":"https://doi.org/10.1145/1985374.1985382","url":null,"abstract":"Effective component testing (or commonly termed as Unit Testing) is important to control defect slippage into the testing stage. Often testing teams lack in-process visibility into the effectiveness of ongoing component testing. Using project data such as code coverage and schedule and effort estimates, we generate temporal and rate-based insights into component testing effectiveness. A simple composite metric is used for measuring and forecasting the health of the component testing process. The early warning signals, based on the forecast and associated insights, lead teams to take proactive actions for improving component testing. In our ongoing experimental studies, we have observed that use of these insights cause a substantial reduction in defect slippage.","PeriodicalId":103819,"journal":{"name":"Workshop on Emerging Trends in Software Metrics","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115903939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fault prediction models usually employ software metrics which were previously shown to be a strong predictor for defects, e.g., SLOC. However, metrics are usually defined on a microlevel (method, class, package), and should therefore be aggregated in order to provide insights in the evolution at the macro-level (system). In addition to traditional aggregation techniques such as the mean, median, or sum, recently econometric aggregation techniques, such as the Gini, Theil, and Hoover indices have been proposed. In this paper we wish to understand whether the aggregation technique influences the presence and strength of the relation between SLOC and defects. Our results indicate that correlation is not strong, and is influenced by the aggregation technique.
{"title":"By no means: a study on aggregating software metrics","authors":"Bogdan Vasilescu, Alexander Serebrenik, M. Brand","doi":"10.1145/1985374.1985381","DOIUrl":"https://doi.org/10.1145/1985374.1985381","url":null,"abstract":"Fault prediction models usually employ software metrics which were previously shown to be a strong predictor for defects, e.g., SLOC. However, metrics are usually defined on a microlevel (method, class, package), and should therefore be aggregated in order to provide insights in the evolution at the macro-level (system). In addition to traditional aggregation techniques such as the mean, median, or sum, recently econometric aggregation techniques, such as the Gini, Theil, and Hoover indices have been proposed. In this paper we wish to understand whether the aggregation technique influences the presence and strength of the relation between SLOC and defects. Our results indicate that correlation is not strong, and is influenced by the aggregation technique.","PeriodicalId":103819,"journal":{"name":"Workshop on Emerging Trends in Software Metrics","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123222365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rachel Burrows, F. Ferrari, Alessandro F. Garcia, François Taïani
Coupling metrics received increased recognition by object-oriented (OO) software developers when they were found to be indicators of important quality attributes, such as fault-proneness. However, there is no consensus on which coupling metrics are effective quality indicators for emerging development paradigms, such as Aspect-Oriented Programming (AOP). AOP aims to improve software quality by providing significantly different decomposition mechanisms, such as pointcut, advice and intertype declarations. Therefore, it is not obvious if quality indicators for AOP can be derived from direct extensions of classical OO metrics. However, empirical studies of AOP do often rely on classical coupling metrics. Despite the recent adoption of AOP in industrial projects, coupling metrics have been rarely evaluated as useful indicators of fault-proneness in this context. This paper analyses the effectiveness of coupling metrics as indicators of fault-proneness in aspect-oriented (AO) systems. We collected faults from several releases of a real-world AO system. We applied and compared existing metrics for coupling and other internal attributes. We have also considered a novel metric that quantifies specific dependencies in AO software not captured by existing metrics. The results show that coupling metrics, which are not directives of object-oriented metrics, tended to be superior indicators of fault-proneness.
{"title":"An empirical evaluation of coupling metrics on aspect-oriented programs","authors":"Rachel Burrows, F. Ferrari, Alessandro F. Garcia, François Taïani","doi":"10.1145/1809223.1809231","DOIUrl":"https://doi.org/10.1145/1809223.1809231","url":null,"abstract":"Coupling metrics received increased recognition by object-oriented (OO) software developers when they were found to be indicators of important quality attributes, such as fault-proneness. However, there is no consensus on which coupling metrics are effective quality indicators for emerging development paradigms, such as Aspect-Oriented Programming (AOP). AOP aims to improve software quality by providing significantly different decomposition mechanisms, such as pointcut, advice and intertype declarations. Therefore, it is not obvious if quality indicators for AOP can be derived from direct extensions of classical OO metrics. However, empirical studies of AOP do often rely on classical coupling metrics. Despite the recent adoption of AOP in industrial projects, coupling metrics have been rarely evaluated as useful indicators of fault-proneness in this context. This paper analyses the effectiveness of coupling metrics as indicators of fault-proneness in aspect-oriented (AO) systems. We collected faults from several releases of a real-world AO system. We applied and compared existing metrics for coupling and other internal attributes. We have also considered a novel metric that quantifies specific dependencies in AO software not captured by existing metrics. The results show that coupling metrics, which are not directives of object-oriented metrics, tended to be superior indicators of fault-proneness.","PeriodicalId":103819,"journal":{"name":"Workshop on Emerging Trends in Software Metrics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123564416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Understanding and measuring how groups of developers collaborate on software projects can provide valuable insight into software quality and the software development process. Current practices of measuring developer collaboration (e.g. with social network analysis) usually employ metrics based on version control change log data to determine who is working on which part of the system. Version control change logs, however, do not tell the whole story. Information about the collaborative problem-solving process is also documented in the issue tracking systems that record solutions to failures, feature requests, or other development tasks. To enrich the data gained from version control change logs, we propose two annotations to be used in issue tracking systems: solution originator and solution approver. We examined the online discussions of 602 issues from the OpenMRS healthcare web application, annotating which developers were the originators of the solution to the issue, or were the approvers of the solution. We used these annotations to augment the version control change logs and found 47 more contributors to the OpenMRS project than the original 40 found in the version control change logs. Applying social network analysis to the data, we found that central developers in a developer network have a high likelihood of being approvers. These results indicate that using our two issue tracking annotations identify project collaborators that version control change logs miss. However, in the absence of our annotations, developer network centrality can be used as an estimate of the project's solution approvers. This improvement in developer activity metrics provides a valuable connection between what we can measure in the project development artifacts and the team's problem-solving process.
{"title":"Improving developer activity metrics with issue tracking annotations","authors":"Andrew Meneely, Mackenzie Corcoran, L. Williams","doi":"10.1145/1809223.1809234","DOIUrl":"https://doi.org/10.1145/1809223.1809234","url":null,"abstract":"Understanding and measuring how groups of developers collaborate on software projects can provide valuable insight into software quality and the software development process. Current practices of measuring developer collaboration (e.g. with social network analysis) usually employ metrics based on version control change log data to determine who is working on which part of the system. Version control change logs, however, do not tell the whole story. Information about the collaborative problem-solving process is also documented in the issue tracking systems that record solutions to failures, feature requests, or other development tasks. To enrich the data gained from version control change logs, we propose two annotations to be used in issue tracking systems: solution originator and solution approver. We examined the online discussions of 602 issues from the OpenMRS healthcare web application, annotating which developers were the originators of the solution to the issue, or were the approvers of the solution. We used these annotations to augment the version control change logs and found 47 more contributors to the OpenMRS project than the original 40 found in the version control change logs. Applying social network analysis to the data, we found that central developers in a developer network have a high likelihood of being approvers. These results indicate that using our two issue tracking annotations identify project collaborators that version control change logs miss. However, in the absence of our annotations, developer network centrality can be used as an estimate of the project's solution approvers. This improvement in developer activity metrics provides a valuable connection between what we can measure in the project development artifacts and the team's problem-solving process.","PeriodicalId":103819,"journal":{"name":"Workshop on Emerging Trends in Software Metrics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129589915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Micro patterns are class-level patterns which aim to identify and formalize common programming techniques. A type (either a class or an interface) is an instance of a micro pattern if and only if all of its methods and/or attributes satisfy the constraints specified by the micro pattern. We suggest a novel approach to the detection of micro patterns which is aimed to identify types that are very close and similar to a correct micro pattern implementation, even if some of the methods and/or attributes of the type do not comply with the constraints defined by the micro pattern. The new interpretation is based on the number of attributes (NOA) and the number of methods (NOM) of a type. The identification of types similar to micro patterns allows the analysis of software systems along various releases, as well as the identification of possible critical classes that can't be detected with a precise matching approach.
{"title":"Metrics-based detection of micro patterns","authors":"S. Maggioni, F. Fontana","doi":"10.1145/1809223.1809229","DOIUrl":"https://doi.org/10.1145/1809223.1809229","url":null,"abstract":"Micro patterns are class-level patterns which aim to identify and formalize common programming techniques. A type (either a class or an interface) is an instance of a micro pattern if and only if all of its methods and/or attributes satisfy the constraints specified by the micro pattern.\u0000 We suggest a novel approach to the detection of micro patterns which is aimed to identify types that are very close and similar to a correct micro pattern implementation, even if some of the methods and/or attributes of the type do not comply with the constraints defined by the micro pattern. The new interpretation is based on the number of attributes (NOA) and the number of methods (NOM) of a type. The identification of types similar to micro patterns allows the analysis of software systems along various releases, as well as the identification of possible critical classes that can't be detected with a precise matching approach.","PeriodicalId":103819,"journal":{"name":"Workshop on Emerging Trends in Software Metrics","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129407360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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