Sequential recommendation problems have received increasing attention in research during the past few years, leading to the inception of a large variety of algorithmic approaches. In this work, we explore how large language models (LLMs), which are nowadays introducing disruptive effects in many AI-based applications, can be used to build or improve sequential recommendation approaches. Specifically, we devise and evaluate three approaches to leverage the power of LLMs in different ways. Our results from experiments on two datasets show that initializing the state-of-the-art sequential recommendation model BERT4Rec with embeddings obtained from an LLM improves NDCG by 15-20% compared to the vanilla BERT4Rec model. Furthermore, we find that a simple approach that leverages LLM embeddings for producing recommendations, can provide competitive performance by highlighting semantically related items. We publicly share the code and data of our experiments to ensure reproducibility.1

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The ICSE 2021 paper titled 'PyCG: Practical Call Graph Generation in Python' comes with a replication package with the purpose of providing open access to (1) our prototype call graph generator, namely PyCG, and (2) the data and scripts that replicate the results of the paper. The Artifact Evaluation Committee found that this package leads to the reproduction of the results outlined in the paper and is openly available1. The replication package contains the following: 1) A Docker image which can be either built manually or downloaded from DockerHub. It contains the source code and installation of PyCG, as well as the installations of two other call graph generators (i.e., Pyan and Depends), which we compare PyCG with. 2) A micro-benchmark suite of 112 Python modules (Section I-A). 3) A macro-benchmark suite of 5 popular Python packages (Section I-B). 4) Python and Bash scripts used to execute PyCG, Pyan and Depends against the micro- and macro-benchmarks and compare the corresponding results.@en

Context: An excessive number of code smells make a software system hard to evolve and maintain. Machine learning methods, in addition to metric-based and heuristic-based methods, have been recently applied to detect code smells; however, current methods are considered far from mature. Objective: First, explore the feasibility of applying deep learning models to detect smells without extensive feature engineering. Second, investigate the possibility of applying transfer-learning in the context of detecting code smells. Methods: We train smell detection models based on Convolution Neural Networks and Recurrent Neural Networks as their principal hidden layers along with autoencoder models. For the first objective, we perform training and evaluation on C# samples, whereas for the second objective, we train the models from C# code and evaluate the models over Java code samples and vice-versa. Results: We find it feasible to detect smells using deep learning methods though the models’ performance is smell-specific. Our experiments show that transfer-learning is definitely feasible for implementation smells with performance comparable to that of direct-learning. This work opens up a new paradigm to detect code smells by transfer-learning especially for the programming languages where the comprehensive code smell detection tools are not available.

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We present a dataset of open source software developed mainly by enterprises rather than volunteers. This can be used to address known generalizability concerns, and, also, to perform research on open source business software development. Based on the premise that an enterprise's employees are likely to contribute to a project developed by their organization using the email account provided by it, we mine domain names associated with enterprises from open data sources as well as through white-and blacklisting, and use them through three heuristics to identify 17 264 enterprise GitHub projects. We provide these as a dataset detailing their provenance and properties. A manual evaluation of a dataset sample shows an identification accuracy of 89%. Through an exploratory data analysis we found that projects are staffed by a plurality of enterprise insiders, who appear to be pulling more than their weight, and that in a small percentage of relatively large projects development happens exclusively through enterprise insiders.

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Memory operations are critical to an application's reliability and performance. To reason about their correctness and track opportunities for optimisations, sophisticated instrumentation frameworks, such as Valgrind and Pin, have been developed. Both provide only limited facilities for analysing the collected data. This work presents a Valgrind's extension for examining a software applications' dynamic memory profile through live interactive analysis with SQL. The Pico COllections Query Library (pico ql) module maps Valgrind's data structures that contain the instrumented application's memory operations metadata to a relational interface. Queries are type-safe and the module imposes only a trivial overhead when idle. The authors evaluate the proposed approach on ten applications and through a qualitative study. They find 900 kb of undefined bytes in bzip2 that account for 12% of its total memory use and a performance-critical code execution path in the Unix commands sort and uniq. The referenced functions are part of glibc and have been independently modified to boost the library's performance. The qualitative study has users rate the usefulness, usability, effort, correctness, and expressiveness of PICO QL queries compared to Python scripts. The findings indicate that querying with PICO QL incurs lower user effort.

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JavaScript is one of the web's key building blocks. It is used by the majority of web sites and it is supported by all modern browsers. We present the first large-scale study of client-side JavaScript code over time. Specifically, we have collected and analyzed a dataset containing daily snapshots of JavaScript code coming from Alexa's Top 10000 web sites (~7.5 GB per day) for nine consecutive months, to study different temporal aspects of web client code. We found that scripts change often; typically every few days, indicating a rapid pace in web applications development. We also found that the lifetime of web sites themselves, measured as the time between JavaScript changes, is also short, in the same time scale. We then performed a qualitative analysis to investigate the nature of the changes that take place. We found that apart from standard changes such as the introduction of new functions, many changes are related to online configuration management. In addition, we examined JavaScript code reuse over time and especially the widespread reliance on third-party libraries. Furthermore, we observed how quality issues evolve by employing established static analysis tools to identify potential software bugs, whose evolution we tracked over time. Our results show that quality issues seem to persist over time, while vulnerable libraries tend to decrease.

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Tracking long-term progress in engineering and applied science allows us to take stock of things we have achieved, appreciate the factors that led to them, and set realistic goals for where we want to go. We formulate seven hypotheses associated with the long term evolution of C programming in the Unix operating system, and examine them by extracting, aggregating, and synthesising metrics from 66 snapshots obtained from a synthetic software configuration management repository covering a period of four decades. We found that over the years developers of the Unix operating system appear to have evolved their coding style in tandem with advancements in hardware technology, promoted modularity to tame rising complexity, adopted valuable new language features, allowed compilers to allocate registers on their behalf, and reached broad agreement regarding code formatting. The progress we have observed appears to be slowing or even reversing prompting the need for new sources of innovation to be discovered and followed.

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Security bugs are critical programming errors that can lead to serious vulnerabilities in software. Examining their behaviour and characteristics within a software ecosystem can provide the research community with data regarding their evolution, persistence and others. We present a dataset that we produced by applying static analysis to the Maven Central Repository (approximately 265GB of data) in order to detect potential security bugs. For our analysis we used FindBugs, a tool that examines Java bytecode to detect numerous types of bugs. The dataset contains the metrics’ results that FindBugs reports for every project version (a JAR) included in the ecosystem. For every version in our data repository, we also store specific metadata, such as the JAR’s size, its dependencies and others. Our dataset can be used to produce interesting research results involving security bugs, as we show in specific examples. @en

Examining a large number of software artifacts can provide the research community with data regarding quality and design. We present a dataset obtained by statically analyzing 22730 jar files taken from the Maven central archive, which is the de-facto application library repository for the Java ecosystem. For our analysis we used three popular static analysis tools that calculate metrics regarding object-oriented design, program size, and package design. The dataset contains the metrics results that every tool reports for every selected jar of the ecosystem. Our dataset can be used to produce interesting research results, such as measure the domain-specific language usage.

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J% is an extension of the Java programming language that efficiently supports the integration of domain-specific languages. In particular, J% allows the embedding of domain-specific language code into Java programs in a syntax-checked and type-safe manner. This paper presents Js support for the sql language. J% checks the syntax and semantics of sql statements at compile-time. It supports query validation against a database schema or through execution to a live database server. The J% compiler generates code that uses standard jdbc api calls, enhancing runtime efficiency and security against sql injection attacks.

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PiCO QL is an open source C/C++ software whose scientific scope is real-time interactive analysis of in-memory data through SQL queries. It exposes a relational view of a system's or application's data structures, which is queryable through SQL. While the application or system is executing, users can input queries through a web-based interface or issue web service requests. Queries execute on the live data structures through the respective relational views. PiCO QL makes a good candidate for ad-hoc data analysis in applications and for diagnostics in systems settings. Applications of PiCO QL include the Linux kernel, the Valgrind instrumentation framework, a GIS application, a virtual real-time observatory of stellar objects, and a source code analyser.@en

Query formalisms and facilities have received significant attention in the past decades resulting in the development of query languages with varying characteristics; many of them resemble sql. Query facilities typically ship as part of database management systems or, sometimes, bundled with programming languages. For applications written in imperative programming languages, database management systems impose an expensive model transformation. In-memory data structures can represent sophisticated relationships in a manner that is efficient in terms of storage and processing overhead, but most general purpose programming languages lack an interpreter and/or an expressive query language for manipulating interactive queries. Issuing interactive ad-hoc queries on program data structures is tough. This work presents a method and an implementation for representing an application’s arbitrary imperative programming data model as a queryable relational one. The Pico COllections Query Library (pico ql) uses a domain specific language to define a relational representation of application data structures and an sql interface implementation. Queries are issued interactively and are type safe. We demonstrate our relational representation for objects and the library’s usefulness on three large c++ projects. pico ql enhances query expressiveness and boosts productivity compared to querying via traditional programming constructs.@en

Context: Numerous factors drive long term progress in programming practices. Goal: We study the evolution of C programming in the Unix operating system. Method: We extract, aggregate, and synthesize metrics from 66 snapshots obtained from an artificial software configuration management repository tracking the evolution of the Unix operating system over four decades. Results: C language programming practices appear to evolve over long term periods; our study identified some continuous trends with highly significant coefficients of determination. Many trends point toward increasing code quality through adherence to numerous programming guidelines, while some others indicate adoption that has reached maturity. In the area of commenting progress appears to have stalled. Conclusions: Studying the long term evolution of programming practices identifies areas where progress has been achieved along an expected path, as well as cases where there is room for improvement.

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State of the art kernel diagnostic tools like DTrace and Systemtap provide a procedural interface for expressing analysis tasks. We argue that a relational interface to kernel data structures can offer complementary benefits for kernel diagnostics. This work contributes a method and an implementation for mapping a kernel's data structures to a relational interface. The Pico COllections Query Library (PiCO QL) Linux kernel module uses a domain specific language to define a relational representation of accessible Linux kernel data structures, a parser to analyze the definitions, and a compiler to implement an SQL interface to the data structures. It then evaluates queries written in SQL against the kernel's data structures. PiCO QL queries are interactive and type safe. Unlike SystemTap and DTrace, PiCO QL is less intrusive because it does not require kernel instrumentation; instead it hooks to existing kernel data structures through the module's source code. PiCO QL imposes no overhead when idle and needs only access to the kernel data structures that contain relevant information for answering the input queries. We demonstrate PiCO QL's usefulness by presenting Linux kernel queries that provide meaningful custom views of system resources and pinpoint issues, such as security vulnerabilities and performance problems.@en

Examining software ecosystems can provide the research community with data regarding artifacts, processes, and communities. We present a dataset obtained from the Maven central repository ecosystem (approximately 265gb of data) by statically analyzing the repository to detect potential software bugs. For our analysis we used FindBugs, a tool that examines Java bytecode to detect numerous types of bugs. The dataset contains the metrics results that FindBugs reports for every project version (a jar) included in the ecosystem. For every version we also stored specific metadata such as the jar's size, its dependencies and others. Our dataset can be used to produce interesting research results, as we show in specific examples.@en

The action required to stem the environmental and social implications of climate change depends crucially on how humankind shapes technology, economy, lifestyle and policy. With transport CO₂ emissions accounting for about a quarter of the total, we examine the contribution of CO₂ output by scientific travel. Thankfully for the reputation of the scientific community, CO₂ emissions associated with the trips required to present a paper at a scientific conference account for just 0.003% of the yearly total. However, with CO₂ emissions for a single conference trip amounting to 7% of an average individual's total CO₂ emissions, scientists should lead by example by demonstrating leadership in addressing the issue.

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Background. Security bugs are critical programming errors that can lead to serious vulnerabilities in software. Such bugs may allow an attacker to take over an application, steal data or prevent the application from working at all. Aim. We used the projects stored in the Maven repository to study the characteristics of security bugs individually and in relation to other software bugs. Specifically, we studied the evolution of security bugs through time. In addition, we examined their persistence and their relationship with a) the size of the corresponding version, and b) other bug categories. Method. We analyzed every project version of the Maven repository by using FindBugs, a popular static analysis tool. To see how security bugs evolve over time we took advantage of the repository's project history and dependency data. Results. Our results indicate that there is no simple rule governing the number of security bugs as a project evolves. In particular, we cannot say that across projects security-related defect counts increase or decrease significantly over time. Furthermore, security bugs are not eliminated in a way that is particularly different from the other bugs. In addition, the relation of security bugs with a project's size appears to be different from the relation of the bugs coming from other categories. Finally, even if bugs seem to have similar behaviour, severe security bugs seem to be unassociated with other bug categories. Conclusions. Our findings indicate that further research should be done to analyze the evolution of security bugs. Given the fact that our experiment included only Java projects, similar research could be done for another ecosystem. Finally, the fact that projects have their own idiosyncrasies concerning security bugs, could help us find the common characteristics of the projects where security bugs increase over time.@en

We explore how programs written in ten popular programming languages are affected by small changes of their source code. This allows us to analyze the extend to which these languages allow the detection of simple errors at compile or at run time. Our study is based on a diverse corpus of programs written in several programming languages systematically perturbed using a mutation-based fuzz generator. The results we obtained prove that languages with weak type systems are significantly likelier than languages that enforce strong typing to let fuzzed programs compile and run, and, in the end, produce erroneous results. More importantly, our study also demonstrates the potential of comparative language fuzz testing for evaluating programming language designs.@en

Purpose - The purpose of this paper is to propose a generic approach that prevents a specific class of code injection attacks (CIAs) in a novel way. Design/methodology/approach - To defend against CIAs this approach involves detecting attacks by using location-specific signatures to validate code statements. The signatures are unique identifiers that represent specific characteristics of a statement's execution. The key property that differentiates the scheme presented in this paper is that these characteristics do not depend entirely on the code statement, but also take into account elements from its execution context. Findings - The approach was applied successfully to defend against attacks targeting structured query language (SQL), XML Path Language and JavaScript with positive results. Originality/value - Despite many countermeasures that have been proposed the number of CIAs has been increasing. Malicious users seem to find new ways to introduce compromised embedded executable code to applications by using a variety of languages and techniques. Hence, a generic approach that defends against such attacks would be a useful countermeasure. This approach can defend attacks that involve both domain-specific languages (e.g. SQL) and general purpose languages (e.g. JavaScript) and can be used both against client-side and server-side attacks.@en