Software Design Flaws Research Articles

Python Code Smell Refactoring Route Generation Based on Association Rule and Correlation

Code smell is a software quality problem caused by software design flaws. Refactoring code smells can improve software maintainability. While prior works mostly focused on Java code smells, only a few prior researches detect and refactor code smells of Python. Therefore, we intend to outline a route (i.e. sequential refactoring operation) for refactoring Python code smells, including LC, LM, LMC, LPL, LSC, LBCL, LLF, MNC, CCC and LTCE. The route could instruct developers to save effort by refactoring the smell strongly correlated with other smells in advance. As a result, more smells could be resolved by a single refactoring. First, we reveal the co-occurrence and the inter-causation between smells. Then, we evaluate the smells’ correlation. Results highlight seven groups of smells with high co-occurrence. Meanwhile, 10 groups of smells correlate with each other in a significant level of Spearman’s correlation coefficient at 0.01. Finally, we generate the refactoring route based on the association rules, we exploit an empirical verification with 10 developers involved. The results of Kendall’s Tau show that the proposed refactoring route has a high inter-agreement with the developer’s perception. In conclusion, we propose four refactoring routes to provide guidance for practitioners, i.e. {LPL [Formula: see text] LLF}, {LPL [Formula: see text] LBCL}, {LPL [Formula: see text] LMC} and {LPL [Formula: see text] LM [Formula: see text] LC [Formula: see text] CCC [Formula: see text] MNC}.

Java Software Development with Event-B: A Practical Guide

Abstract The cost of fixing software design flaws after the completion of a software product is so high that it is vital to come up with ways to detect software design flaws in the early stages of ...

Scavenger Hunt: An Empirical Method for Mobile Collaborative Problem-Solving

Pervasive computing enables field researchers to accomplish efficient field-work in teams that they once performed in isolation, over several trips, or not at all. Field researchers can deduce new information from findings they make while in the field, and apply it immediately to the situation at hand. This is especially important in fields where the time or resources to conduct several studies isn't available. This domain can be termed mobile collaborative problem-solving. Mobile collaborative problem-solving is a young domain of study, and not much is known about how people can collaborate effectively in the field using computers. We suggest a scavenger hunt model that provides a viable prototyping method. A scavenger hunt is a typical mobile activity that both adults and children can perform. The ultimate goal was to use a scavenger hunt to unearth the software's design flaws