lunch-talks

Type4Py: Deep Similarity Learning-Based Type Inference for Python (Amir Mir) Dynamic languages, such as Python and Javascript, trade static typing for developer flexibility. While this allegedly enables greater productivity, lack of static typing can cause runtime exceptions, type inconsistencies, and is a major factor for weak IDE support. To alleviate these issues, PEP 484 introduced optional type annotations for Python. As retrofitting types to existing codebases is error-prone and laborious, learning-based approaches have been proposed to enable automatic type annotations based on existing, partially annotated codebases.

Abstract: Quantum Software Development is the process of conceiving, specifying, designing, programming, documenting, and testing executable quantum programs that are meant to run on practical quantum hardware. Even though Quantum Software Development research has gained traction over the years, it is still mainly focused on problem analysis, language design, and implementation. Software testing, which is the process of executing a program or application with the intent of finding failures, and verifying that the software product is fit for use is yet to receive substantial attention in quantum software development.

An Exploratory Study of Log Placement Recommendation in an Enterprise System (Jeanderson Barros Cândido) Logging is a development practice that plays an important role in the operations and monitoring of complex systems. Developers place log statements in the source code and use log data to understand how the system behaves in production. Unfortunately, anticipating where to log during development is challenging. Previous studies show the feasibility of leveraging machine learning to recommend log placement despite the data imbalance since logging is a fraction of the overall code base.

Topic 1: Developer-Friendly Test Amplification: The Interplay between Automatic Generation and Human Exploration (Carolin Brandt) Caro gives some insights on her first PhD project & we can learn from it: Automatically generating test cases for software is an active research topic for many years. However, the powerful tools we create are up until now rarely used in practice. Starting from issues reported by developers in previous studies, we investigate what aspects are important to design test generation approaches that developers appreciate.

Topic 1: What if(True)? Testing your ML models with metamorphic transformations on Code (Leonhard Applis) What does a deep neural network, such as CodeBERT, really learn? The metrics are State of the Art - but we have no clue why. With our last project Annibale and me looked into ways to alter the test-set to gain more information on what is happening in that beautiful model of yours. We cover everything your heart desires: Machine Learning, Code, Testing and Statistics.

Abstract: The ACM SIGSOFT Paper and Peer Review Quality Initiative, which I will present in this talk, seeks to increase paper quality, review quality, acceptance rates and consensus around research practices by generating and evolving empirical standards. An Empirical Standard is a brief public document that communicates expectations for a given kind of study. Empirical standards contain lists of specific practices or attributes related to a particular method, e.g., “uses random assignment” (experiment) or “presents clear chain of evidence from interviewee quotations to proposed concepts” (qualitative survey).

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.

Abstract: Continuous Integration (CI) is a software development practice that enables developers to build reliable software faster. Given its proven benefits, most organizations have started adopting CI and fostered the application of several principles, such as commit often, that reduce conflicts in the team and ensure that the build is continuously executable. However, when facing a deadline or a difficult task, developers can deviate from such best practices and follow anti-patterns, which are ineffective solutions to recurrent problems.