Mining Sequences of Developer Interactions in Visual Studio for Usage Smells

In this paper, we present a semi-automatic approach for mining a large-scale dataset of IDE interactions to extract usage smells, i.e., inefficient IDE usage patterns exhibited by developers in the field. The approach outlined in this paper first mines frequent IDE usage patterns, filtered via a set of thresholds and by the authors, that are subsequently supported (or disputed) using a developer survey, in order to form usage smells. In contrast with conventional mining of IDE usage data, our approach identifies time-ordered sequences of developer actions that are exhibited by many developers in the field. This pattern mining workflow is resilient to the ample noise present in IDE datasets due to the mix of actions and events that these datasets typically contain. We identify usage patterns and smells that contribute to the understanding of the usability of Visual Studio for debugging, code search, and active file navigation, and, more broadly, to the understanding of developer behavior during these software development activities. Among our findings is the discovery that developers are reluctant to use conditional breakpoints when debugging, due to perceived IDE performance problems as well as due to the lack of error checking in specifying the conditional

Improving Efficiency of Incremental Mining by Trie Structure and Pre-Large Itemsets

Incremental data mining has been discussed widely in recent years, as it has many practical applications, and various incremental mining algorithms have been proposed. Hong et al. proposed an efficient incremental mining algorithm for handling newly inserted transactions by using the concept of pre-large itemsets. The algorithm aimed to reduce the need to rescan the original database and also cut maintenance costs. Recently, Lin et al. proposed the Pre-FUFP algorithm to handle new transactions more efficiently, and make it easier to update the FP-tree. However, frequent itemsets must be mined from the FP-growth algorithm. In this paper, we propose a Pre-FUT algorithm (Fast-Update algorithm using the Trie data structure and the concept of pre-large itemsets), which not only builds and updates the trie structure when new transactions are inserted, but also mines all the frequent itemsets easily from the tree. Experimental results show the good performance of the proposed algorithm

A Review on: Efficient Method for Mining Frequent Itemsets on Temporal Data

Temporal data can hold time-stamped information that affects the results of data mining. Customary strategies for finding frequent itemsets accept that datasets are static; also the instigated rules are relevant over the whole dataset. In any case, this is not the situation when data is temporal. The work is done to enhance the proficiency of mining frequent itemsets on temporal data. The patterns can hold in either all or, then again a portion of the intervals. It proposes another method with respect to time interval is called as frequent itemsets mining with time cubes. The concentration is building up an efficient algorithm for this mining issue by broadening the notable a priori algorithm. The thought of time cubes is proposed to handle different time hierarchies. This is the route by which the patterns that happen intermittently, amid a time interval or both, are perceived. Another thickness limit is likewise proposed to take care of the overestimating issue of time periods and furthermore ensure that found patterns are valid

A Review on: Efficient Method for Mining Frequent Itemsets on Temporal Data

Temporal data can hold time-stamped information that affects the results of data mining. Customary strategies for finding frequent itemsets accept that datasets are static; also the instigated rules are relevant over the whole dataset. In any case, this is not the situation when data is temporal. The work is done to enhance the proficiency of mining frequent itemsets on temporal data. The patterns can hold in either all or, then again a portion of the intervals. It proposes another method with respect to time interval is called as frequent itemsets mining with time cubes. The concentration is building up an efficient algorithm for this mining issue by broadening the notable a priori algorithm. The thought of time cubes is proposed to handle different time hierarchies. This is the route by which the patterns that happen intermittently, amid a time interval or both, are perceived. Another thickness limit is likewise proposed to take care of the overestimating issue of time periods and furthermore ensure that found patterns are valid