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  1. AU="Sotiropoulos, Thodoris"
  2. AU="Osborne, Cameron"
  3. AU="Seira, Jordan"
  4. AU="Komiya, Ryota"
  5. AU="Yeonsoo Joe"
  6. AU="Christina Drake"
  7. AU="Rosenbloom, E Scott"
  8. AU="Clémentine Schilte"
  9. AU="Montanari, Andrea"
  10. AU="Salehi Karizmeh, Mojtaba"
  11. AU="Svanberg Frisinger, Frida"
  12. AU="Iyappan, Petchi"
  13. AU="Naomi Nakagata"
  14. AU="Marianne A. van der Sande"
  15. AU="Reno, Chiara"

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  1. Buch ; Online: Extended Paper

    Sotiropoulos, Thodoris / Chaliasos, Stefanos / Su, Zhendong

    API-driven Program Synthesis for Testing Static Typing Implementations

    2023  

    Abstract: We introduce a novel approach for testing static typing implementations based on the concept of API-driven program synthesis. The idea is to synthesize type-intensive but small and well-typed programs by leveraging and combining application programming ... ...

    Abstract We introduce a novel approach for testing static typing implementations based on the concept of API-driven program synthesis. The idea is to synthesize type-intensive but small and well-typed programs by leveraging and combining application programming interfaces (APIs) derived from existing software libraries. Our primary insight is backed up by real-world evidence: a significant number of compiler typing bugs are caused by small test cases that employ APIs from the standard library of the language under test. This is attributed to the inherent complexity of the majority of these APIs, which often exercise a wide range of sophisticated type-related features. The main contribution of our approach is the ability to produce small client programs with increased feature coverage, without bearing the burden of generating the corresponding well-formed API definitions from scratch. To validate diverse aspects of static typing procedures (i.e., soundness, precision of type inference), we also enrich our API-driven approach with fault-injection and semantics-preserving modes, along with their corresponding test oracles. We evaluate our implemented tool, Thalia on testing the static typing implementations of the compilers for three popular languages, namely, Scala, Kotlin, and Groovy. Thalia has uncovered 84 typing bugs (77 confirmed and 22 fixed), most of which are triggered by test cases featuring APIs that rely on parametric polymorphism, overloading, and higher-order functions. Our comparison with state-of-the-art shows that Thalia yields test programs with distinct characteristics, offering additional and complementary benefits.
    Schlagwörter Computer Science - Programming Languages ; Computer Science - Software Engineering
    Thema/Rubrik (Code) 005
    Erscheinungsdatum 2023-11-08
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  2. Buch ; Online: Search Engine Similarity Analysis

    Dritsa, Konstantina / Sotiropoulos, Thodoris / Skarpetis, Haris / Louridas, Panos

    A Combined Content and Rankings Approach

    2020  

    Abstract: How different are search engines? The search engine wars are a favorite topic of on-line analysts, as two of the biggest companies in the world, Google and Microsoft, battle for prevalence of the web search space. Differences in search engine popularity ... ...

    Abstract How different are search engines? The search engine wars are a favorite topic of on-line analysts, as two of the biggest companies in the world, Google and Microsoft, battle for prevalence of the web search space. Differences in search engine popularity can be explained by their effectiveness or other factors, such as familiarity with the most popular first engine, peer imitation, or force of habit. In this work we present a thorough analysis of the affinity of the two major search engines, Google and Bing, along with DuckDuckGo, which goes to great lengths to emphasize its privacy-friendly credentials. To do so, we collected search results using a comprehensive set of 300 unique queries for two time periods in 2016 and 2019, and developed a new similarity metric that leverages both the content and the ranking of search responses. We evaluated the characteristics of the metric against other metrics and approaches that have been proposed in the literature, and used it to (1) investigate the similarities of search engine results, (2) the evolution of their affinity over time, (3) what aspects of the results influence similarity, and (4) how the metric differs over different kinds of search services. We found that Google stands apart, but Bing and DuckDuckGo are largely indistinguishable from each other.

    Comment: Shorter version of this paper was accepted in the 21st International Conference on Web Information Systems Engineering (WISE 2020). The final authenticated version is available online at https://doi.org/10.1007/978-3-030-62008-0_2
    Schlagwörter Computer Science - Information Retrieval ; Computer Science - Machine Learning
    Thema/Rubrik (Code) 303
    Erscheinungsdatum 2020-11-01
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  3. Buch ; Online: Identifying Bugs in Make and JVM-Oriented Builds

    Sotiropoulos, Thodoris / Chaliasos, Stefanos / Mitropoulos, Dimitris / Spinellis, Diomidis

    2020  

    Abstract: Incremental and parallel builds are crucial features of modern build systems. Parallelism enables fast builds by running independent tasks simultaneously, while incrementality saves time and computing resources by processing the build operations that ... ...

    Abstract Incremental and parallel builds are crucial features of modern build systems. Parallelism enables fast builds by running independent tasks simultaneously, while incrementality saves time and computing resources by processing the build operations that were affected by a particular code change. Writing build definitions that lead to error-free incremental and parallel builds is a challenging task. This is mainly because developers are often unable to predict the effects of build operations on the file system and how different build operations interact with each other. Faulty build scripts may seriously degrade the reliability of automated builds, as they cause build failures, and non-deterministic and incorrect build results. To reason about arbitrary build executions, we present buildfs, a generally-applicable model that takes into account the specification (as declared in build scripts) and the actual behavior (low-level file system operation) of build operations. We then formally define different types of faults related to incremental and parallel builds in terms of the conditions under which a file system operation violates the specification of a build operation. Our testing approach, which relies on the proposed model, analyzes the execution of single full build, translates it into buildfs, and uncovers faults by checking for corresponding violations. We evaluate the effectiveness, efficiency, and applicability of our approach by examining hundreds of Make and Gradle projects. Notably, our method is the first to handle Java-oriented build systems. The results indicate that our approach is (1) able to uncover several important issues (245 issues found in 45 open-source projects have been confirmed and fixed by the upstream developers), and (2) orders of magnitude faster than a state-of-the-art tool for Make builds.
    Schlagwörter Computer Science - Software Engineering
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2020-05-14
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  4. Buch ; Online: PyCG

    Salis, Vitalis / Sotiropoulos, Thodoris / Louridas, Panos / Spinellis, Diomidis / Mitropoulos, Dimitris

    Practical Call Graph Generation in Python

    2021  

    Abstract: Call graphs play an important role in different contexts, such as profiling and vulnerability propagation analysis. Generating call graphs in an efficient manner can be a challenging task when it comes to high-level languages that are modular and ... ...

    Abstract Call graphs play an important role in different contexts, such as profiling and vulnerability propagation analysis. Generating call graphs in an efficient manner can be a challenging task when it comes to high-level languages that are modular and incorporate dynamic features and higher-order functions. Despite the language's popularity, there have been very few tools aiming to generate call graphs for Python programs. Worse, these tools suffer from several effectiveness issues that limit their practicality in realistic programs. We propose a pragmatic, static approach for call graph generation in Python. We compute all assignment relations between program identifiers of functions, variables, classes, and modules through an inter-procedural analysis. Based on these assignment relations, we produce the resulting call graph by resolving all calls to potentially invoked functions. Notably, the underlying analysis is designed to be efficient and scalable, handling several Python features, such as modules, generators, function closures, and multiple inheritance. We have evaluated our prototype implementation, which we call PyCG, using two benchmarks: a micro-benchmark suite containing small Python programs and a set of macro-benchmarks with several popular real-world Python packages. Our results indicate that PyCG can efficiently handle thousands of lines of code in less than a second (0.38 seconds for 1k LoC on average). Further, it outperforms the state-of-the-art for Python in both precision and recall: PyCG achieves high rates of precision ~99.2%, and adequate recall ~69.9%. Finally, we demonstrate how PyCG can aid dependency impact analysis by showcasing a potential enhancement to GitHub's "security advisory" notification service using a real-world example.
    Schlagwörter Computer Science - Programming Languages ; Computer Science - Software Engineering
    Thema/Rubrik (Code) 005
    Erscheinungsdatum 2021-02-28
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  5. Buch ; Online: Detecting Missing Dependencies and Notifiers in Puppet Programs

    Sotiropoulos, Thodoris / Mitropoulos, Dimitris / Spinellis, Diomidis

    2019  

    Abstract: Puppet is a popular computer system configuration management tool. It provides abstractions that enable administrators to setup their computer systems declaratively. Its use suffers from two potential pitfalls. First, if ordering constraints are not ... ...

    Abstract Puppet is a popular computer system configuration management tool. It provides abstractions that enable administrators to setup their computer systems declaratively. Its use suffers from two potential pitfalls. First, if ordering constraints are not specified whenever an abstraction depends on another, the non-deterministic application of abstractions can lead to race conditions. Second, if a service is not tied to its resources through notification constructs, the system may operate in a stale state whenever a resource gets modified. Such faults can degrade a computing infrastructure's availability and functionality. We have developed an approach that identifies these issues through the analysis of a Puppet program and its system call trace. Specifically, we present a formal model for traces, which allows us to capture the interactions of Puppet abstractions with the file system. By analyzing these interactions we identify (1) abstractions that are related to each other (e.g., operate on the same file), and (2) abstractions that should act as notifiers so that changes are correctly propagated. We then check the relationships from the trace's analysis against the program's dependency graph: a representation containing all the ordering constraints and notifications declared in the program. If a mismatch is detected, our system reports a potential fault. We have evaluated our method on a large set of Puppet modules, and discovered 57 previously unknown issues in 30 of them. Benchmarking further shows that our approach can analyze in minutes real-world configurations with a magnitude measured in thousands of lines and millions of system calls.
    Schlagwörter Computer Science - Software Engineering
    Thema/Rubrik (Code) 005
    Erscheinungsdatum 2019-05-27
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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