  Proper incentives are important for motivating developers in open-source
communities, which is crucial for maintaining the development of open-source
software healthy. To provide such incentives, an accurate and objective
developer contribution measurement method is needed. However, existing methods
rely heavily on manual peer review, lacking objectivity and transparency. The
metrics of some automated works about effort estimation use only syntax-level
or even text-level information, such as changed lines of code, which lack
robustness. Furthermore, some works about identifying core developers provide
only a qualitative understanding without a quantitative score or have some
project-specific parameters, which makes them not practical in real-world
projects. To this end, we propose CValue, a multidimensional information
fusion-based approach to measure developer contributions. CValue extracts both
syntax and semantic information from the source code changes in four
dimensions: modification amount, understandability, inter-function and
intra-function impact of modification. It fuses the information to produce the
contribution score for each of the commits in the projects. Experimental
results show that CValue outperforms other approaches by 19.59% on 10
real-world projects with manually labeled ground truth. We validated and proved
that the performance of CValue, which takes 83.39 seconds per commit, is
acceptable to be applied in real-world projects. Furthermore, we performed a
large-scale experiment on 174 projects and detected 2,282 developers having
inflated commits. Of these, 2,050 developers did not make any syntax
contribution; and 103 were identified as bots.
