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Failure of One, Fall of Many: An Exploratory Study of Software Features for Defect Prediction 20

Failure of One, Fall of Many: An Exploratory Study of Softwa...

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20th ieee International working conference on source code analysis and manipulation (SCAM)

作者： dos Santos, Geanderson Esteves Figueiredo, Eduardo Fed Univ Minas Gerais UFMG Dept Comp Sci DCC Belo Horizonte MG Brazil

ISBN: (纸本)9781728192482

Software defect prediction represents an area of interest in both academia and the software industry. Thus, software defects are prevalent in software development and might generate numerous difficulties for users and developers apart. The current literature offers multiple alternative approaches to predict the likelihood of defects in the source code. Most of these studies concentrate on predicting defects from a broad set of software features. As a result, the individual discriminating power of software features is still unknown as some perform well only with specific projects or metrics. In this study, we applied machine learning techniques in a popular dataset. This data has information about software defects in five Java projects, containing 5,371 classes and 37 software features. To this aim, we convey an exploratory investigation that produced hundreds of thousands of machine learning models from a diverse collection of software features. These models are random in the sense that they promptly select the features from the entire pool of features. Even though the immense majority of models are ineffective, we could produce several models that yield accurate predictions, thus classifying defects from Java project classes. Among these accurate models, our results indicate that change metric features are more present than entropy or class-level metrics. We concentrated our analysis on models that rank a randomly chosen defective class higher than a casually selected clean class with over 80% accuracy. We also report and discuss some features contributing to the explanation of model decisions. Therefore, our study promotes reasoning on which features support predicting defects in these projects. Finally, we present the implications of our work to practitioners.

关键词： defect prediction explainability source code metrics

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Supporting Static Program analysis and Transformation of Quantum-Based Languages

Supporting Static Program Analysis and Transformation of Qua...

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Quantum Computing and Engineering (QCE), ieee International conference on

作者： Joshua A.C. Behler Ali F. Al-Ramadan Betis Baheri Qiang Guan Jonathan I. Maletic Computer Science Department Kent State University Kent Ohio USA

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

The work aims to enable the use of common software engineering techniques and tools for quantum programming languages (e.g., OpenQASM). With the increased interest in quantum computing, researchers are adopting the use of higher-level quantum programming languages versus low-level circuit diagrams. While general purpose programming languages (e.g., C++, Python) are highly supported by a variety of software engineering tools, these novel programming languages for quantum computing have almost no support. Useable tools for debugging, static analysis, error detection, and transformation are currently non-existent. This work extends an existing software infrastructure (i.e., srcML) for the analysis, exploration, and manipulation of source code to OpenQASM. The srcML infrastructure, via parsing, generates abstract syntax information of programs to support high-level querying and analysis of the source code. With this, quantum developers can extract information and identify possible errors or inefficiencies in their programs. The paper presents the basic syntactic markup for OpenQASM. Also, a number of relevant quantum-based problems (e.g., iteration patterns, control recursion) are described and examples of how they are addressed using srcML are given.

关键词： Quantum computing source coding Static analysis Debugging C++ languages Syntactics Software Data mining Software engineering Python

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An Approach for the Identification of Information Leakage in Automotive Infotainment systems 20

An Approach for the Identification of Information Leakage in...

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20th ieee International working conference on source code analysis and manipulation (SCAM)

作者： Moiz, Abdul Alalfi, Manar H. Ryerson Univ Dept Comp Sci Toronto ON Canada

ISBN: (纸本)9781728192482

The advancements in the digitization world has revolutionized the automotive industry. Today's modern cars are equipped with internet, computers that can provide autonomous driving functionalities as well as infotainment systems that can run mobile operating systems, like Android Auto and Apple CarPlay. Android Automotive is Google's android operating system tailored to run natively on vehicle's infotainment systems, it allows third party apps to be installed and run on vehicle's infotainment systems. Such apps may raise security concerns related to user's safety, security and privacy. This paper investigates security concerns of in-vehicle apps, specifically, those related to inter component communication (ICC) among these apps. ICC allows apps to share information via inter or intra apps components through a messaging object called intent. In case of insecure communication, Intent can be hijacked or spoofed by malicious apps and user's sensitive information can be leaked to hacker's database. We investigate the attack surface and vulnerabilities in these apps and provide a static analysis approach and a tool to find data leakage vulnerabilities. The approach can also provide hints to mitigate these leaks. We evaluate our approach by analyzing a set of Android Auto apps downloaded from Google Play store, and we report our validated results on vulnerabilities identified on those apps.

关键词： Android Automotive TXL Static analysis API Security Information Flow Control

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The Role of Implicit Conversions in Erroneous Function Argument Swapping in C plus 20

The Role of Implicit Conversions in Erroneous Function Argum...

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20th ieee International working conference on source code analysis and manipulation (SCAM)

作者： Szalay, Richard Sinkovics, Abel Porkolab, Zoltan Eotvos Lorand Univ Fac Informat Dept Programming Languages & Compilers Budapest Hungary

ISBN: (纸本)9781728192482

Argument selection defects, in which the programmer has chosen the wrong argument to a function call is a widely investigated problem. The compiler can detect such misuse of arguments based on the argument and parameter type in case of statically typed programming languages. When adjacent parameters have the same type, or they can be converted between one another, the potential error will not be diagnosed. Related research is usually confined to exact type equivalence, often ignoring potential implicit or explicit conversions. However, in current mainstream languages, like C++, built-in conversions between numerics and user-defined conversions may significantly increase the number of mistakes to go unnoticed. We investigated the situation for C and C++ languages where functions are defined with multiple adjacent parameters that allow arguments to pass in the wrong order. When implicit conversions are taken into account, the number of mistake-prone function declarations significantly increases compared to strict type equivalence. We analysed the outcome and categorised the offending parameter types. The empirical results should further encourage the language and library development community to emphasise the importance of strong typing and the restriction of implicit conversion.

关键词： static analysis function parameters argument selection defect type safety strong typing error-prone constructs C plus plus programming language

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Incremental Flow analysis through Computational Dependency Reification 20

Incremental Flow Analysis through Computational Dependency R...

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20th ieee International working conference on source code analysis and manipulation (SCAM)

作者： Van der Plas, Jens Stievenart, Quentin Van Es, Noah De Roover, Coen Vrije Univ Brussel Software Languages Lab Brussels Belgium

ISBN: (纸本)9781728192482

Static analyses are used to gain more confidence in changes made by developers. To be of most use, such analyses must deliver feedback fast. Therefore, incremental static analyses update previous results rather than entirely recompute them. This reduces the analysis time upon a program change, and makes the analysis well-suited for environments where the code base is frequently updated, such as in IDEs and CI pipelines. In this work, we present a general approach to render a modular static analysis for highly dynamic programs incremental, by exploiting dependencies between intermediate analysis results. Modular analyses divide a program in interdependent parts that are analysed in isolation. The dependencies between these parts stem, for example, from the use of shared variables within the program. Our incrementalisation approach leverages the modularity of the analysis together with the dependencies that it reifies to compute and bound the impact of changes. This way, only the affected parts of the result need to be reanalysed, and unnecessary recomputations are avoided. We apply our approach to both a function-modular and a thread-modular analysis and evaluate it by comparing an incremental update of an existing result to a full reanalysis. We find reductions of the analysis time from 6% to 99% on 14 out of 16 benchmark programs, and on most programs the impact on precision is limited. On 7 of the programs, reanalysis time is reduced by more than 75%, showing that our approach results in fast incremental updates.

关键词： Static Program analysis Incremental analysis Modular analysis Dynamic Languages

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Does Infrastructure as code Adhere to Semantic Versioning? An analysis of Ansible Role Evolution 20

Does Infrastructure as Code Adhere to Semantic Versioning? A...

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20th ieee International working conference on source code analysis and manipulation (SCAM)

作者： Opdebeeck, Ruben Zerouali, Ahmed Velazquez-Rodriguez, Camilo De Roover, Coen Vrije Univ Brussel Brussels Belgium

ISBN: (纸本)9781728192482

Ansible, a popular Infrastructure-as-code platform, provides reusable collections of tasks called roles. Roles are often contributed by third parties, and like general-purpose libraries, they evolve. As such, new releases of roles need to be tagged with version numbers, for which Ansible recommends adhering to the semantic versioning format. However, roles significantly differ from general-purpose libraries, and it is not yet known what constitutes a breaking change or the addition of a feature to a role. Consequently, this can cause confusion for clients of a role and new role contributors. To alleviate this issue, we perform an empirical study on semantic versioning in Ansible roles to uncover the types of changes that trigger certain types of version bumps. We collect a dataset of over 70 000 version increments spanning upwards of 7 800 Ansible roles. Moreover, we design a novel structural model for these roles, and implement a domain-specific structural change extraction algorithm to calculate structural difference metrics. Afterwards, we quantitatively investigate the state of semantic versioning in Ansible roles and identify the most commonly changed components. Then, using the structural difference metrics, we train a Random Forest classifier to predict applicable version bumps for Ansible role releases. Lastly, we confirm our empirical findings with a developer survey. Our observations show that although most Ansible role developers follow the semantic versioning format, it appears that they do not always consistently follow the same rules when selecting the version bump to apply.

关键词： Ansible Infrastructure as code Semantic Versioning empirical study mining software repositories

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Enhancing Recommendations of Composite Refactorings based on the Practice

Enhancing Recommendations of Composite Refactorings based on...

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ieee International Workshop on source code analysis and manipulation

作者： Ana Carla Bibiano Daniel Coutinho Anderson Uchôa Wesley K. G. Assunção Alessandro Garcia Rafael de Mello Thelma E. Colanzi Daniel Tenório Audrey Vasconcelos Baldoino Fonseca Márcio Ribeiro Pontifical Catholic University of Rio de Janeiro Brazil Federal University of Ceará Brazil North Carolina State University USA Federal University of Rio de Janeiro Brazil State University of Maringá Brazil Federal University of Alagoas Brazil

ISBN: (数字)9798331528508

ISBN: (纸本)9798331528515

Refactoring is a non-trivial maintenance activity. Developers spend time and effort refactoring code to remove structural problems, i.e., code smells. Recent studies indicated that developers often apply composite refactoring (composite, for short), i.e., two or more interrelated refactorings. However, prior studies revealed that only 10% of composite refactorings are considered complete, i.e., those fully removing code smells. Many incomplete refactorings can even replace or introduce smells, requiring additional effort for their removal later in the project. Moreover, existing refactoring recommendations are not well-detailed and do not alert developers about these possible side effects. To address these gaps, we conducted a large-scale study involving more than 250k refactorings from 42 software projects, including both open-source and closed-source projects. Our goal is to investigate how the most common complete composites are combined and their side effects in the practice. Our results reveal that the current recommendation to apply Extract Method(s) with fine-grained refactoring types needs refinements. We found that certain fine-grained refactorings like Change Variable Types and Change Return Types can introduce up to 45% of Brain Methods when combined with Extract Method(s). Moreover, Ex-tract Method(s) and Move Method(s), a common recommendation to remove Feature Envy, may inadvertently introduce about 30% of Lazy Classes and approximately 70% of Data Classes. Despite these potential side effects, existing refactoring catalogs and tools' recommenders do not alert developers about these side effects. Finally, we consolidate our findings into a catalog to provide clear guidance for developers and researchers on effectively applying composite refactorings to fully remove code smells.

关键词： codes source coding Feature extraction Software Maintenance

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Static Extraction of Enforced Authorization Policies SEEAUTHZ 20

Static Extraction of Enforced Authorization Policies SEEAUTH...

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20th ieee International working conference on source code analysis and manipulation (SCAM)

作者： Berger, Bernhard J. Nguempnang, Rodrigue Wete Sohr, Karsten Koschke, Rainer Univ Bremen Software Engn Grp Bremen Germany Univ Bremen Technol Zentrum Informat & Informat Tech Bremen Germany

ISBN: (纸本)9781728192482

Authorization is an intrinsic part of a software's security. Determining whether a user is allowed to access a resource or not is crucial, not only in safety-critical applications but also in everyday applications to prevent misuse of data or software. There is plenty of research dealing with validating and verifying authorization policies in the security community. Still, an implemented authorization policy does not necessarily match the planned authorization policy, i.e., even a validated and verified authorization policy can pose security issues when implemented incorrectly. This gap between planned and implemented authorization policy poses the risk of unauthorized access to sensitive resources due to insufficient authorization checks. Therefore, it is essential to ensure a system's security to validate the implemented authorization policy against the planned one. We, therefore, describe the authorization pattern and present an algorithm to extract authorization graphs from implemented authorization policies, which can then be used to compare against the planned authorization policy. To that end, we developed a configurable context-sensitive analysis tailored to Java-based software systems, where the context is the authorization facts that hold on each point. Using a configuration for Apache Shiro, a security library that supports authorization, we evaluated our implementation using an open-source repository system for the management and dissemination of digital content and a closed-source manufacturing execution system. We discuss additional usage scenarios of the analysis results and describe how to transfer the approach to other authorization policies and programming languages.

关键词： security authorization static analysis

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Visualizing source code as Comics Using Generative AI

Visualizing Source Code as Comics Using Generative AI

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ieee International Workshop on Visualizing Software for Understanding and analysis (VISSOFT)

作者： David Heidrich Andreas Schreiber Institute for Software Technology German Aerospace Center (DLR) Weβling Germany Institute for Software Technology German Aerospace Center (DLR) Köln Germany

The architecture and inner structure of software is often only implicitly available in the form of its source code and thus not tangible and intuitively easy to understand for non-programmers and laymen. Our goal is to create visualizations as automatically as possible, with which such people can neverthe-less understand the software or parts of the software and get a feel for the structure of the software and how its methods work. Especially for newcomers to software projects, for management or even for students and pupils, it can be helpful to get a non-technical insight into the software. We use the concept of visualizing information as comics to present aspects of the software as strikingly as possible, as comics are an effective way to present complex systems and interrelationships for certain target groups. For this purpose, we present a method to generate comics from source code. Our semi-automated process is based on generating a prompt for an LLM from source code, which in turn generates a prompt for a comic image generation using the text-to-image model Stable Diffusion. We show that generative AI methods can be used to rapidly generate human-compatible artistic representations from source code. However, further research is needed to validate the understandability of the results.

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Lint-Based Warnings in Python code: Frequency, Awareness and Refactoring

Lint-Based Warnings in Python Code: Frequency, Awareness and...

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ieee International Workshop on source code analysis and manipulation

作者： Naelson Oliveira Márcio Ribeiro Rodrigo Bonifácio Rohit Gheyi Igor Wiese Baldoino Fonseca Federal University of Alagoas (UFAL) Maceió Alagoas Brasília University (UnB) Brasília Federal University of Campina Grande (UFCG) Campina Grande Paraíba Federal University of Technology – Paraná (UTFPR) Curitiba Paraná

ISBN: (纸本)9781665496100

Python is a popular programming language characterized by its simple syntax and easy learning curve. Like many languages, Python has a set of best practices that should be followed to avoid bugs and improve other quality attributes (such as maintenance and readability). In this context, non-compliance to these practices can be detected by using linting tools. Previous work conducted studies to better understand the frequency of a class of problems that can be found using Python linters: warnings, here named as lint-based warnings. However, they either rely on small datasets or focus on few domains, such as machine learning or web-systems projects. In this paper, we provide a mixed-method study where we analyze the frequency of six lint-based warnings in 1,119 different open-source general-purpose Python projects. To go further, we also conduct a survey to check whether developers are aware of the lint-based warnings we study here. In particular, we intend to check whether they are able to identify the six lint-based warnings. To remove the lint-based warnings, we suggest the application of simple refactorings. Last but not least, we evaluate the suggestions by submitting pull requests to remove lint-based warnings from open-source projects. Our results show that 39% of the 1,119 projects have at least one lint-based warning. After analyzing the survey data, we also show that developers prefer Python code without lint-based warnings. Regarding the pull requests, we achieve a 71.8% of acceptance rate.

关键词： codes source coding Computer bugs Machine learning Syntactics Maintenance engineering Best practices

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