3,043 research outputs found
Organizing the Technical Debt Landscape
To date, several methods and tools for detecting source code and design anomalies have been developed. While each method focuses on identifying certain classes of source code anomalies that potentially relate to technical debt (TD), the overlaps and gaps among these classes and TD have not been rigorously demonstrated. We propose to construct a seminal technical debt landscape as a way to visualize and organize research on the subjec
Combining Spreadsheet Smells for Improved Fault Prediction
Spreadsheets are commonly used in organizations as a programming tool for
business-related calculations and decision making. Since faults in spreadsheets
can have severe business impacts, a number of approaches from general software
engineering have been applied to spreadsheets in recent years, among them the
concept of code smells. Smells can in particular be used for the task of fault
prediction. An analysis of existing spreadsheet smells, however, revealed that
the predictive power of individual smells can be limited. In this work we
therefore propose a machine learning based approach which combines the
predictions of individual smells by using an AdaBoost ensemble classifier.
Experiments on two public datasets containing real-world spreadsheet faults
show significant improvements in terms of fault prediction accuracy.Comment: 4 pages, 1 figure, to be published in 40th International Conference
on Software Engineering: New Ideas and Emerging Results Trac
SpreadCluster: Recovering Versioned Spreadsheets through Similarity-Based Clustering
Version information plays an important role in spreadsheet understanding,
maintaining and quality improving. However, end users rarely use version
control tools to document spreadsheet version information. Thus, the
spreadsheet version information is missing, and different versions of a
spreadsheet coexist as individual and similar spreadsheets. Existing approaches
try to recover spreadsheet version information through clustering these similar
spreadsheets based on spreadsheet filenames or related email conversation.
However, the applicability and accuracy of existing clustering approaches are
limited due to the necessary information (e.g., filenames and email
conversation) is usually missing. We inspected the versioned spreadsheets in
VEnron, which is extracted from the Enron Corporation. In VEnron, the different
versions of a spreadsheet are clustered into an evolution group. We observed
that the versioned spreadsheets in each evolution group exhibit certain common
features (e.g., similar table headers and worksheet names). Based on this
observation, we proposed an automatic clustering algorithm, SpreadCluster.
SpreadCluster learns the criteria of features from the versioned spreadsheets
in VEnron, and then automatically clusters spreadsheets with the similar
features into the same evolution group. We applied SpreadCluster on all
spreadsheets in the Enron corpus. The evaluation result shows that
SpreadCluster could cluster spreadsheets with higher precision and recall rate
than the filename-based approach used by VEnron. Based on the clustering result
by SpreadCluster, we further created a new versioned spreadsheet corpus
VEnron2, which is much bigger than VEnron. We also applied SpreadCluster on the
other two spreadsheet corpora FUSE and EUSES. The results show that
SpreadCluster can cluster the versioned spreadsheets in these two corpora with
high precision.Comment: 12 pages, MSR 201
Enron versus EUSES: A Comparison of Two Spreadsheet Corpora
Spreadsheets are widely used within companies and often form the basis for
business decisions. Numerous cases are known where incorrect information in
spreadsheets has lead to incorrect decisions. Such cases underline the
relevance of research on the professional use of spreadsheets.
Recently a new dataset became available for research, containing over 15.000
business spreadsheets that were extracted from the Enron E-mail Archive. With
this dataset, we 1) aim to obtain a thorough understanding of the
characteristics of spreadsheets used within companies, and 2) compare the
characteristics of the Enron spreadsheets with the EUSES corpus which is the
existing state of the art set of spreadsheets that is frequently used in
spreadsheet studies.
Our analysis shows that 1) the majority of spreadsheets are not large in
terms of worksheets and formulas, do not have a high degree of coupling, and
their formulas are relatively simple; 2) the spreadsheets from the EUSES corpus
are, with respect to the measured characteristics, quite similar to the Enron
spreadsheets.Comment: In Proceedings of the 2nd Workshop on Software Engineering Methods in
Spreadsheet
An analysis of techniques and methods for technical debt management: a reflection from the architecture perspective
Technical debt is a metaphor referring to the consequences of weak software development. Managing technical debt is necessary in order to keep it under control, and several techniques have been developed with the goal of accomplishing this. However, available techniques have grown disperse and managers lack guidance. This paper covers this gap by providing a systematic mapping of available techniques and methods for technical debt management, covering architectural debt, and identifying existing gaps that prevent to manage technical debt efficiently
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