To what extent can maintenance problems be predicted by code smell detection? – An empirical study

AbstractContext mells are indicators of poor coding and design choices that can cause problems during software maintenance and evolution. ive tudy is aimed at a detailed investigation to which extent problems in maintenance projects can be predicted by the detection of currently known code smells. iple case study was conducted, in which the problems faced by six developers working on four different Java systems were registered on a daily basis, for a period up to four weeks. Where applicable, the files associated to the problems were registered. Code smells were detected in the pre-maintenance version of the systems, using the tools Borland Together and InCode. In-depth examination of quantitative and qualitative data was conducted to determine if the observed problems could be explained by the detected smells. s he total set of problems, roughly 30% percent were related to files containing code smells. In addition, interaction effects were observed amongst code smells, and between code smells and other code characteristics, and these effects led to severe problems during maintenance. Code smell interactions were observed between collocated smells (i.e., in the same file), and between coupled smells (i.e., spread over multiple files that were coupled). sions le 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.