Using open-source packages when developing software applications is the general practice among a vast amount of software developers. However, importing open-source code which may depend on other existing technologies may lead to the appearance of a transitive dependency chain. As a result, failure of packages with a high amount of transitive dependants may have an impact on the performance of all dependant applications. This work focuses on designing a graph data structure which maps the dependency relationship between packages as edges, with nodes representing a single version of a certain package. Moreover, the data structure may perform queries based on time intervals, being able to resolve versions in the same manner as a package distributor would. By constructing such data structure, an analysis of the most critical packages for an ecosystem can be conducted. This paper looks mainly into the Debian ecosystem, and searches for applications which are most critical. Based on this paper criticality evaluation, the package dpkg was found to be both most critical and most used in whole Debian's package main repository.

Multiple benchmarks for question answering (QA) systems often under-represent questions that require lists to be answered, referred to in this work as ListQA. This type of question can provide valuable insights into the system’s ability to structure its internal knowledge. In this work, we introduce TimelineQA (TLQA), a specialized subset of ListQA that requires both list comprehension and temporal understanding to generate correct answers. We automatically curate a benchmark for TLQA and test it against generative large language models (LLMs) commonly employed in QA systems. Our findings reveal significant shortcomings in current models, particularly in their inability to provide complete answers and accurately align facts temporally. To address these issues, we explore how Retrieval Augmented Generation (RAG) can enhance performance in this task. Our results indicate that while RAG improves performance, especially in retrieving relevant contextual information, there remains considerable room for further improvement. Both the benchmark and the evaluation of the models can be publicly accessed here.