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Record W4391559702 · doi:10.1109/tse.2024.3363223

DynAMICS: A Tool-Based Method for the Specification and Dynamic Detection of Android Behavioral Code Smells

2024· article· en· W4391559702 on OpenAlex
Dimitri Prestat, Naouel Moha, Roger Villemaire, Florent Avellaneda

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Software Engineering · 2024
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCode smellComputer scienceAndroid (operating system)Source codeSoftwareCode (set theory)Software qualityProgramming languageArtificial intelligenceSoftware engineeringSoftware developmentOperating system

Abstract

fetched live from OpenAlex

Code smells are the result of poor design choices within software systems that complexify source code and impede evolution and performance. Therefore, detecting code smells within software systems is an important priority to decrease technical debt. Furthermore, the emergence of mobile applications (apps) has brought new types of Android-specific code smells, which relate to limitations and constraints on resources like memory, performance and energy consumption. Among these Android-specific smells are those that describe inappropriate behaviour during the execution that may negatively impact software quality. Static analysis tools, however, show limitations for detecting these behavioural code smells and properly detecting behavioural code smells requires considering the dynamic behaviour of the apps. To dynamically detect behavioural code smells, we hence propose three contributions : (1) A method, the D <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ynamics</small> method, a step-by-step method for the specification and dynamic detection of Android behavioural code smells; (2) A tool, the D <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ynamics</small> tool, implementing this method on seven code smells; and (3) A validation of our approach on 538 apps from F-D <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">roid</small> with a comparison with the static analysis detection tools, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</small> D <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">octor</small> and P <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">aprika</small> , from the literature. Our method consists of four steps: (1) the specification of the code smells, (2) the instrumentation of the app, (3) the execution of the apps, and (4) the detection of the behavioural code smells. Our results show that many instances of code smells that cannot be detected with static detection tools are indeed detected with our dynamic approach with an average precision of 92.8% and an average recall of 53.4%.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.765
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.291
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it