Comparing software metrics tools

Lincke, Rüdiger; Lundberg, Jonas; Löwe, Welf

doi:10.1145/1390630.1390648

Cited by 147 publications

(88 citation statements)

References 9 publications

(13 reference statements)

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“…We consider distances between purely discrete (nonprobabilistic, untimed) systems, and our distances are directed rather than symmetric (based on simulation rather than bisimulation). Software metrics measure properties such as lines of code, depth of inheritance (in an object-oriented language), number of bugs in a module or the time it took to discover the bugs (see for example [12,16]). These functions measure syntactic properties of the source code, and are fundamentally different from our distance functions that capture the difference in the behavior (semantics) of programs.…”

Section: Introductionmentioning

confidence: 99%

Simulation Distances

Černý

Henzinger

Radhakrishna

2010

CONCUR 2010 - Concurrency Theory

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Abstract. Boolean notions of correctness are formalized by preorders on systems. Quantitative measures of correctness can be formalized by realvalued distance functions between systems, where the distance between implementation and specification provides a measure of "fit" or "desirability." We extend the simulation preorder to the quantitative setting, by making each player of a simulation game pay a certain price for her choices. We use the resulting games with quantitative objectives to define three different simulation distances. The correctness distance measures how much the specification must be changed in order to be satisfied by the implementation. The coverage distance measures how much the implementation restricts the degrees of freedom offered by the specification. The robustness distance measures how much a system can deviate from the implementation description without violating the specification. We consider these distances for safety as well as liveness specifications. The distances can be computed in polynomial time for safety specifications, and for liveness specifications given by weak fairness constraints. We show that the distance functions satisfy the triangle inequality, that the distance between two systems does not increase under parallel composition with a third system, and that the distance between two systems can be bounded from above and below by distances between abstractions of the two systems. These properties suggest that our simulation distances provide an appropriate basis for a quantitative theory of discrete systems. We also demonstrate how the robustness distance can be used to measure how many transmission errors are tolerated by error correcting codes.

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Section: Introductionmentioning

confidence: 99%

Simulation Distances

Černý

Henzinger

Radhakrishna

2010

CONCUR 2010 - Concurrency Theory

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“…In fact, we have shown earlier that different metrics tools lead to different class level quality values for the same system(s) and the same quality model and even to different conclusions regarding the quality ranking of the classes [27].…”

Section: ) Variable Selectionmentioning

confidence: 98%

“…We use VizzAnalyzer for the metrics extraction, but other software metrics tools (cf. Lincke et al for an overview [27]) could have been used as well. The VizzAnalyzer was our choice since it supports automated processes: an interface (c) allows for batch processing of a list of projects and an export engine (d) to store the computed metrics in a database for later processing.…”

Section: Instrumentationmentioning

confidence: 99%

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Software Quality Prediction Models Compared

Lincke

Gutzmann

Löwe

2010

2010 10th International Conference on Quality Software

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Abstract-Numerous empirical studies confirm that many software metrics aggregated in software quality prediction models are valid predictors for qualities of general interest like maintainability and correctness. Even these general quality models differ quite a bit, which raises the question: Do the differences matter? The goal of our study is to answer this question for a selection of quality models that have previously been published in empirical studies. We compare these quality models statistically by applying them to the same set of software systems, i.e., to altogether 328 versions of 11 open-source software systems. Finally, we draw conclusions from quality assessment using the different quality models, i.e., we calculate a quality trend and compare these conclusions statistically. We identify significant differences among the quality models. Hence, the selection of the quality model has influence on the quality assessment of software based on software metrics.

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“…Static analysis tools that extract source measures can have different methods and approaches for quantifying certain properties (Lincke et al, 2008). As a result, they may produce inconsistent values for measures.…”

Section: Validity Threatsmentioning

confidence: 99%

Handover of managerial responsibilities in global software development: a case study of source code evolution and quality

2014

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Context: Studies report on the negative effect on quality in global software development (GSD) due to communication and coordination related challenges. However, empirical studies reporting on the magnitude of the effect are scarce. Research Method: This paper presents findings from an embedded explanatory case study on the change in quality over time, across multiple releases, for products that were developed in a GSD setting. The GSD setting involved periods of distributed development between geographically dispersed sites as well as a handover of project management responsibilities between the involved sites. Investigations were performed on two medium-sized products from a company that is part of a large multinational corporation. Quality is investigated quantitatively using defect data, and measures that quantify two source code properties, size and complexity. Observations were triangulated with subjective views from company representatives. Results and Findings: There were no observable indications that the distribution of work or handover of project management responsibilities had an impact on quality on both products. Among the product-, process-, and people-related success factors, we identified welldesigned product architectures, early handover planning, support from the sending site to the receiving site after the handover, and skilled employees at the involved sites. Overall, these results can be useful input for decision-makers who are considering distributing development work between globally dispersed sites or handing over project management responsibilities from one site to another. Moreover, our study shows that analyzing the evolution of size and complexity properties of a product's source code can provide valuable information to support decision-making during similar projects. Finally, the strategy used by the company to relocate responsibilities can also be considered as an alternative for software transfers, which have been linked with a decline in efficiency, productivity and quality.

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Comparing software metrics tools

Cited by 147 publications

References 9 publications

Simulation Distances

Simulation Distances

Software Quality Prediction Models Compared

Handover of managerial responsibilities in global software development: a case study of source code evolution and quality

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