1,721,119 research outputs found
How do generative models draw a software engineer? An empirical study on implicit bias of open-source image generation models
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Detection of Reverse Engineering Activities Before the Attack
No full textIn typical cybersecurity scenarios, one aims at detecting attacks after the fact: in this work, we aim at applying an active defence, by detecting activities of attackers trying to analyse and reverse engineer the code of an Android app, before they will be able to perform an attack by tampering with the application code. We instrumented an app to collect various runtime data before and after deployment, in normal behaviour and under malicious analysis. We introduce the concept of partial execution paths as subsets of a program trace suddenly interrupted, as possible indicators of debugging activities. Such clues, along with system calls sequences and delays between them, stack information, and sensors data, are all data that are collected to help our system in deciding whether our app is under analysis and its device has to be considered compromised
Elephant56: Design and implementation of a parallel genetic algorithms framework on hadoop mapreduce
No full textelephant56 is an open source framework for the development and execution of single and parallel Genetic Algorithms (GAs). It provides high level functionalities that can be reused by developers, who no longer need to worry about complex internal structures. In particular, it offers the possibility of distributing the GAs computation over a Hadoop MapReduce cluster of multiple computers. In this paper we describe the design and the implementation details of the framework that supports three different models for parallel GAs, namely the global model, the grid model and the island model. Moreover, we provide a complete example of use
How Multi-objective Genetic Programming is Effective for Software Development Effort Estimation?
Full text linkDevelop, deploy and execute parallel genetic algorithms in the cloud
No full textMaking Genetic Algorithms (GAs) distributed in an on-demand fashion involves different phases from resources allocation to actual deployment and execution. We propose a cloud architecture with a conceptual workflow able to cover each GAs distribution phase
Web effort estimation: the value of cross-company data set compared to single-company data set
Full text linkThis study investigates to what extent Web effort estimation models built using cross-company data sets can provide suitable effort estimates for Web projects belonging to another company, when compared to Web effort estimates obtained using that company's own data on their past projects (single-company data set). It extends a previous study (S3) where these same research questions were investigated using data on 67 Web projects from the Tukutuku database. Since S3 was carried out, data on other 128 Web projects was added to Tukutuku; therefore this study uses the entire set of 195 projects from the Tukutuku database, which now also includes new data from other single-company data sets. Predictions between cross-company and single-company models are compared using Manual Stepwise Regression+Linear Regression and Case-Based Reasoning. In addition, we also investigated to what extent applying a filtering mechanism to cross-company datasets prior to building prediction models can affect the accuracy of the effort estimates they provide. The present study corroborates the conclusions of S3 since the cross-company models provided much worse predictions than the single-company models. Moreover, the use of the filtering mechanism significantly improved the prediction accuracy of cross-company models when estimating single-company projects, making it comparable to that using single-company datasets
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