A Literature Survey on Empirical Evidence in Software Engineering

Pek, Ekaterina; Lämmel, Ralf

doi:10.48550/arxiv.1304.1002

Cited by 1 publication

(3 citation statements)

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“…8 See the file documentation/drawBackground.html in the JHotDraw 5.1 distribution. 9 Ekaterina Pek and Ralf Lämmel [32] reviewed the publications of seven major software engineering conferences in 2011/2012 with respect to their usage of corpora, i.e. "collections of software artifacts [...] to derive empirical evidence from".…”

Section: Methods Of Analysis For Potential Bad Smellsmentioning

confidence: 99%

“…Given that a push refactoring would be possible, we need to discuss, whether the addition of the type HandleEnumeration makes sense. 32 The main question is, whether the type pulls it weight. To answer this question we reviewed StandardDrawingView looking for other responsibilities to push.…”

Section: (D6) "Known Type In Aggregation"mentioning

confidence: 99%

“…The methods for maintaining its state would just forward to a Vector inside. In addition, 32 Lieberherr and Holland explore for a given example [21, Figure 7] the option to have a specific class to realize a list and to push responsibilities into this class. In the context of that paper the example has the function to illustrate that a "lifting" refactoring is not always possible so that the "pushing" refactoring is sometimes required.…”

Section: (D6) "Known Type In Aggregation"mentioning

confidence: 99%

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Did JHotDraw respect the Law of Good Style? - An exploratory deep dive into the nature of false positives of bad code smells

Speicher

2020

Programming

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Developers need to make a constant effort to improve the quality of their code if they want to stay productive. Tools that highlight code locations that could benefit from refactoring are thus highly desirable. The most common name for such locations is "bad code smell". A number of tools offer such quality feedback and there is a substantial body of related research.However, all these tools, including those based on Machine Learning, still produce false positives. Every single false positive shown to the developer places a cognitive burden on her and should thus be avoided. The literature discusses the choice of metric thresholds, the general subjectivity of such a judgment and the relation to conscious design choices, "design ideas".To examine false positives and the relation between bad smells and design ideas, we designed and conducted an exploratory case study. While previous research presented a broad overview, we have chosen a narrow setting to reach for even deeper insights: The framework JHotDraw had been designed so thoughtfully that most smell warnings are expected to be false positives. Nevertheless, the "Law of Good Style", better known as the "Law of Demeter", is a rather restrictive design rule so that we still expected to find some potential bad smells, i.e. violations of this "Law".This combination led to 1215 potential smells of which at most 42 are true positives. We found generic as well as specific design ideas that were traded for the smell. Our confidence in that decision ranged from high enough to very high. We were surprised to realize that the smell definition itself required the formulation of constructive design ideas. Finally we found some smells to be the result of the limitation of the language and one could introduce auxiliary constructive design ideas to compensate for them.The decision whether a potential smell occurrence is actually a true positive was made very meticulously. For that purpose we took three qualities that the smell could affect negatively into account and we discussed the result of the recommended refactorings. If we were convinced that we had found a false positive, we described the relationships with design ideas.The realization that not only general design ideas but also specific design ideas have an influence on whether a potential smell is a true positive turns the problem of false positives from a scientific problem ("What is the true definition of the smell?") to a engineering problem ("How can we incorporate design ideas into smell definitions?"). We recommend to add adaptation points to the smell definitions. Higher layers may then adapt the smell for specific contexts. After adaptation the tool may continuously provide distinct and precise quality feedback, reducing the cognitive load for the developer and preventing habituation. Furthermore, the schema for the discussion of potential smells may be used to elaborate more sets of true and false smell occurrences. Finally, it follows that smell detection based on machine learning should also take signs of...

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Section: Methods Of Analysis For Potential Bad Smellsmentioning

confidence: 99%

Section: (D6) "Known Type In Aggregation"mentioning

confidence: 99%

Section: (D6) "Known Type In Aggregation"mentioning

confidence: 99%

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