In Windows CMD:
wmic csproduct get vendor,name,identifyingnumber
Result is smth like:
IdentifyingNumber Name Vendor
JG1Z111 Latitude E6420 Dell Inc.
Results: From the total set of problems, roughly 30% percent were related to files containing code smells. ......
Conclusions: The role of code smells on the overall system maintainability is relatively minor, thus complementary approaches are needed to achieve more comprehensive assessments of maintainability.
Moreover, to improve the explanatory power of code smells, interaction effects amongst collocated smells and coupled smells should be taken into account during analysis.
Twelve different code smells were detected in the systems via Borland Together and InCode.
We report on a multiple case study in which the problems and challenges faced by six developers working on four different Java systems were registered on a daily basis, for a period up to four weeks.
there is a substantial body of work that investigates if certain source code characteristics (i.e., a code smell) affect a given maintenance outcome (e.g., effort, changes, defects).
Individual progress meetings (20–30 min): were conducted daily, with each of the developers and the researcher present at the study to keep track of the progress, and register problems encountered during the project (ex. Dev: ‘‘It took me 3 h to understand this method. . .’’)
Code smells automatically detected in the systems:
Duplicated code in conditional
God (Large) Class
God (Long) Method
Temporary variable for various
Use interface instead of
Interface Segregation Principle
In total, 137 different problems were identified from the differ-
ent maintenance projects. From the total, 64 problems (47%) were
associated to Java source code. The remaining 73 (53%) constituted
problems not directly related to code such as: lack of adequate
technical infrastructure, developer’s coding habits, external ser-
vices, runtime environment, and defects initially present in the
From the total set of difficulties identified during maintenance,
less than half (43%) were related to Java code, and from those, only
58% clearly related to any of the twelve code smells used to analyze
the code. This means that even if we count those difficulties that
are due to combination of factors, roughly only 30% of the total
set of difficulties can be explained and potentially foreseen by code
smells. As a result, we conclude that the subset of aspects that are
covered by current code smell detection has a relatively low
The results from our study reminds us of the limitations of evaluations based purely on static analysis and suggest the need of more comprehensive quality models and techniques that can incorporate the analysis of diverse factors.