1,216 research outputs found
An agent-driven semantical identifier using radial basis neural networks and reinforcement learning
Due to the huge availability of documents in digital form, and the deception
possibility raise bound to the essence of digital documents and the way they
are spread, the authorship attribution problem has constantly increased its
relevance. Nowadays, authorship attribution,for both information retrieval and
analysis, has gained great importance in the context of security, trust and
copyright preservation. This work proposes an innovative multi-agent driven
machine learning technique that has been developed for authorship attribution.
By means of a preprocessing for word-grouping and time-period related analysis
of the common lexicon, we determine a bias reference level for the recurrence
frequency of the words within analysed texts, and then train a Radial Basis
Neural Networks (RBPNN)-based classifier to identify the correct author. The
main advantage of the proposed approach lies in the generality of the semantic
analysis, which can be applied to different contexts and lexical domains,
without requiring any modification. Moreover, the proposed system is able to
incorporate an external input, meant to tune the classifier, and then
self-adjust by means of continuous learning reinforcement.Comment: Published on: Proceedings of the XV Workshop "Dagli Oggetti agli
Agenti" (WOA 2014), Catania, Italy, Sepember. 25-26, 201
Search based software engineering: Trends, techniques and applications
© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives.
This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E
Defect prediction with bad smells in code
Background: Defect prediction in software can be highly beneficial for
development projects, when prediction is highly effective and defect-prone
areas are predicted correctly. One of the key elements to gain effective
software defect prediction is proper selection of metrics used for dataset
preparation. Objective: The purpose of this research is to verify, whether code
smells metrics, collected using Microsoft CodeAnalysis tool, added to basic
metric set, can improve defect prediction in industrial software development
project. Results: We verified, if dataset extension by the code smells sourced
metrics, change the effectiveness of the defect prediction by comparing
prediction results for datasets with and without code smells-oriented metrics.
In a result, we observed only small improvement of effectiveness of defect
prediction when dataset extended with bad smells metrics was used: average
accuracy value increased by 0.0091 and stayed within the margin of error.
However, when only use of code smells based metrics were used for prediction
(without basic set of metrics), such process resulted with surprisingly high
accuracy (0.8249) and F-measure (0.8286) results. We also elaborated data
anomalies and problems we observed when two different metric sources were used
to prepare one, consistent set of data. Conclusion: Extending the dataset by
the code smells sourced metric does not significantly improve the prediction
effectiveness. Achieved result did not compensate effort needed to collect
additional metrics. However, we observed that defect prediction based on the
code smells only is still highly effective and can be used especially where
other metrics hardly be used.Comment: Chapter 10 in Software Engineering: Improving Practice through
Research (B. Hnatkowska and M. \'Smia{\l}ek, eds.), pp. 163-176, 201
Predicting and Evaluating Software Model Growth in the Automotive Industry
The size of a software artifact influences the software quality and impacts
the development process. In industry, when software size exceeds certain
thresholds, memory errors accumulate and development tools might not be able to
cope anymore, resulting in a lengthy program start up times, failing builds, or
memory problems at unpredictable times. Thus, foreseeing critical growth in
software modules meets a high demand in industrial practice. Predicting the
time when the size grows to the level where maintenance is needed prevents
unexpected efforts and helps to spot problematic artifacts before they become
critical.
Although the amount of prediction approaches in literature is vast, it is
unclear how well they fit with prerequisites and expectations from practice. In
this paper, we perform an industrial case study at an automotive manufacturer
to explore applicability and usability of prediction approaches in practice. In
a first step, we collect the most relevant prediction approaches from
literature, including both, approaches using statistics and machine learning.
Furthermore, we elicit expectations towards predictions from practitioners
using a survey and stakeholder workshops. At the same time, we measure software
size of 48 software artifacts by mining four years of revision history,
resulting in 4,547 data points. In the last step, we assess the applicability
of state-of-the-art prediction approaches using the collected data by
systematically analyzing how well they fulfill the practitioners' expectations.
Our main contribution is a comparison of commonly used prediction approaches
in a real world industrial setting while considering stakeholder expectations.
We show that the approaches provide significantly different results regarding
prediction accuracy and that the statistical approaches fit our data best
funcGNN: A Graph Neural Network Approach to Program Similarity
Program similarity is a fundamental concept, central to the solution of
software engineering tasks such as software plagiarism, clone identification,
code refactoring and code search. Accurate similarity estimation between
programs requires an in-depth understanding of their structure, semantics and
flow. A control flow graph (CFG), is a graphical representation of a program
which captures its logical control flow and hence its semantics. A common
approach is to estimate program similarity by analysing CFGs using graph
similarity measures, e.g. graph edit distance (GED). However, graph edit
distance is an NP-hard problem and computationally expensive, making the
application of graph similarity techniques to complex software programs
impractical. This study intends to examine the effectiveness of graph neural
networks to estimate program similarity, by analysing the associated control
flow graphs. We introduce funcGNN, which is a graph neural network trained on
labeled CFG pairs to predict the GED between unseen program pairs by utilizing
an effective embedding vector. To our knowledge, this is the first time graph
neural networks have been applied on labeled CFGs for estimating the similarity
between high-level language programs. Results: We demonstrate the effectiveness
of funcGNN to estimate the GED between programs and our experimental analysis
demonstrates how it achieves a lower error rate (0.00194), with faster (23
times faster than the quickest traditional GED approximation method) and better
scalability compared with the state of the art methods. funcGNN posses the
inductive learning ability to infer program structure and generalise to unseen
programs. The graph embedding of a program proposed by our methodology could be
applied to several related software engineering problems (such as code
plagiarism and clone identification) thus opening multiple research directions.Comment: 11 pages, 8 figures, 3 table
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