A New Family of Software Anti-patterns: Linguistic Anti-patterns

Arnaoudova, Venera; Penta, Massimiliano Di; Antoniol, Giuliano; Gueheneuc, Y.-G.

doi:10.1109/csmr.2013.28

Cited by 65 publications

(48 citation statements)

References 19 publications

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“…Our tool mines the entire change history of r i , checks out each commit in chronological order, and runs an implementation of the DECOR smell detector based on the original rules defined by Moha et al [34]. DECOR identifies smells using detection rules based on the values of internal quality metrics 4 . The choice of using DECOR was driven by the fact that (i) it is a state-of-the-art smell detector having a high accuracy in detecting smells [34]; and (ii) it applies simple detection rules that allow it to be very efficient.…”

Section: B Data Extraction and Analysismentioning

confidence: 99%

“…Generally speaking, software evolution has always been considered as one of the reasons behind "software aging" [38] or "increasing complexity" [28][35] [47]. Broadly speaking, smells can also manifest themselves not only in the source code but also in software lexicons [29], [4], and can even affect other types of artifacts, such as spreadsheets [22], [23] or test cases [9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

When and Why Your Code Starts to Smell Bad

Tufano

Palomba

Bavota

et al. 2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

202

143

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Abstract-In past and recent years, the issues related to managing technical debt received significant attention by researchers from both industry and academia. There are several factors that contribute to technical debt. One of these is represented by code bad smells, i.e., symptoms of poor design and implementation choices. While the repercussions of smells on code quality have been empirically assessed, there is still only anecdotal evidence on when and why bad smells are introduced. To fill this gap, we conducted a large empirical study over the change history of 200 open source projects from different software ecosystems and investigated when bad smells are introduced by developers, and the circumstances and reasons behind their introduction. Our study required the development of a strategy to identify smellintroducing commits, the mining of over 0.5M commits, and the manual analysis of 9,164 of them (i.e., those identified as smellintroducing). Our findings mostly contradict common wisdom stating that smells are being introduced during evolutionary tasks. In the light of our results, we also call for the need to develop a new generation of recommendation systems aimed at properly planning smell refactoring activities.

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Section: B Data Extraction and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

When and Why Your Code Starts to Smell Bad

Tufano

Palomba

Bavota

et al. 2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

202

143

View full text Add to dashboard Cite

show abstract

“…Lexicon bad smells are potential structural problems of identifiers caused by the naming itself or the relation among identifiers. Arnaoudova et al defined anti-patterns for identifier naming [25]. The anti-patterns are represented by the relationships among the signature of a method, identifiers involved in the method body, and the comment of the method.…”

Section: Methods Namingmentioning

confidence: 99%

“…We create an AST-tree for each source file and extract M from the source files. We split method names by using camelCase and snake case heuristics like [25]. We exclude the following methods from M. abstract methods: These methods have very few information for recommendation.…”

Section: Extraction Of Naming Association Rulesmentioning

confidence: 99%

Method Verb Recommendation Using Association Rule Mining in a Set of Existing Projects

Kashiwabara

Ishio

Hata

et al. 2015

IEICE Trans. Inf. & Syst.

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SUMMARYIt is well-known that program readability is important for maintenance tasks. Method names are important identifiers for program readability because they are used for understanding the behavior of methods without reading a part of the program. Although developers can create a method name by arbitrarily choosing a verb and objects, the names are expected to represent the behavior consistently. However, it is not easy for developers to choose verbs and objects consistently since each developer may have a different notion of a suitable lexicon for method names. In this paper, we propose a technique to recommend candidate verbs for a method name so that developers can use various verbs consistently. We recommend candidate verbs likely to be used as a part of a method name, using association rules extracted from existing methods. To evaluate our technique, we have extracted rules from 445 open source projects written in Java and confirmed the accuracy of our approach by applying the extracted rules to several open source applications. As a result, we found that 84.9% of the considered methods in four projects are recommended the existing verb. Moreover, we found that 73.2% of the actual renamed methods in six projects are recommended the correct verb.

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“…Arnaoudova et al [45] defined a new term Linguistic Anti-Patterns to identify recurring poor practices in naming and choosing identifiers. They categorized the anti-pattern into six sub-groups: three for the methods and the other three for the attributes.…”

Section: Detecting Inconsistent Identifiersmentioning

confidence: 99%

Automatic identifier inconsistency detection using code dictionary

Kim

2015

Empir Software Eng

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In the revised version of our manuscript, we have incorporated all the comments and suggestions of the three reviewers as best as we can. This letter includes both the reviewer comments and our responses to these comments. In these responses, we have explained changes made to the manuscript on the basis of the reviewers' suggestions. We have also mentioned the locations in our revised manuscript where the changes have been made to help the reviewers locate these revisions. Wherever possible, we have provided an excerpt of the revised part. Sincerely yours, The Authors Editor's CommentsIn preparing the new revision authors addressed most of the reviewers concerns. The writing style improved and comparison with another tool is now presented.The paper quality was substantially improved, however, there are still a few points authors may want to consider prior to publication. In particular some style issues (reviewer 1) and some concern about the presented examples (see reviewer 3). We understand that sometime it may be more a matter of taste, nevertheless, it is important that examples clearly motivate and properly set the stage for the paper. ⇒ We revised the style and example issues according to the reviewer comments. Please see the following responses.Overall we suggest authors check the style (reviewer 1) and seriously consider comments of reviewer 3 on the paper presented examples, ambiguity and the need of such an approach. ⇒ For the Reviewer 1's comments, we revised all the style issues in Abstract, Section 1, 2, 3, and 4 as suggested. For the Reviewer 3's comments, we tried to convince why we used the examples and how our evaluation process confirmed that the examples are valid. Note that 16 human subjects reviewed the detection results and these are some of examples shown in the paper. Reviewer #1's Comments:The authors substantially improved the manuscript: they added a comparison with existing work, fixed a few errors, and rephrased most of the unclear sentences. ⇒ We appreciate the constructive suggestions and comments.I would suggest a few minor improvements before publication: incFile(): Based on the Javadoc of the method ("Method implements the logic for filtering file name inclusion") this must be expanded to 'include' rather than 'increase' as it is currently in Table 2. ⇒ This example in Table 2 is removed from the row ("inc ⇒ increase") as the reviewer pointed out."inconsistent identifiers frequently and inevitably occurs" "this issue can be arise" "We then conducted a user study to check the validity of these results. This study asked 16 developers to evaluate the precision and recall of detection results." > "To evaluate the precision and recall of detection results we conducted a user study involving 16 developers." "A naming convention defines a specific way in which names identifiers in programs." > to name? ⇒ All these typos and grammar errors are fixed accordingly. Reviewer #2's Comments:The authors have properly addressed my remaining comments and suggestions. In particular, they have perf...

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A New Family of Software Anti-patterns: Linguistic Anti-patterns

Cited by 65 publications

References 19 publications

When and Why Your Code Starts to Smell Bad

When and Why Your Code Starts to Smell Bad

Method Verb Recommendation Using Association Rule Mining in a Set of Existing Projects

Automatic identifier inconsistency detection using code dictionary

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