arXiv subjects

Diego Elias Costa

Publications and source records attributed to Diego Elias Costa.

39 records · Page 3Linked to original sources

arXiv2021

Breaking Type Safety in Go: An Empirical Study on the Usage of the unsafe Package

A decade after its first release, the Go programming language has become a major programming language in the development landscape. While praised for its clean syntax and C-like performance, Go also contains a strong static type-system that prevents arbitrary type casting and arbitrary memory access, making the language type-safe by design. However, to give developers the possibility of implementing low-level code, Go ships with a special package called unsafe that offers developers a way around the type-safety of Go programs. The package gives greater flexibility to developers but comes at a higher risk of runtime errors, chances of non-portability, and the loss of compatibility guarantees for future versions of Go. In this paper, we present the first large-scale study on the usage of the unsafe package in 2,438 popular Go projects. Our investigation shows that unsafe is used in 24% of Go projects, motivated primarily by communicating with operating systems and C code, but is also commonly used as a source of performance optimization. Developers are willing to use unsafe to break language specifications (e.g., string immutability) for better performance and 6% of analyzed projects that use unsafe perform risky pointer conversions that can lead to program crashes and unexpected behavior. Furthermore, we report a series of real issues faced by projects that use unsafe, from crashing errors and non-deterministic behavior to having their deployment restricted from certain popular environments. Our findings can be used to understand how and why developers break type-safety in Go, and help motivate further tools and language development that could make the usage of unsafe in Go even safer.

cs.SE↗

arXiv2021

A Comparison of Natural Language Understanding Platforms for Chatbots in Software Engineering

Chatbots are envisioned to dramatically change the future of Software Engineering, allowing practitioners to chat and inquire about their software projects and interact with different services using natural language. At the heart of every chatbot is a Natural Language Understanding (NLU) component that enables the chatbot to understand natural language input. Recently, many NLU platforms were provided to serve as an off-the-shelf NLU component for chatbots, however, selecting the best NLU for Software Engineering chatbots remains an open challenge. Therefore, in this paper, we evaluate four of the most commonly used NLUs, namely IBM Watson, Google Dialogflow, Rasa, and Microsoft LUIS to shed light on which NLU should be used in Software Engineering based chatbots. Specifically, we examine the NLUs' performance in classifying intents, confidence scores stability, and extracting entities. To evaluate the NLUs, we use two datasets that reflect two common tasks performed by Software Engineering practitioners, 1) the task of chatting with the chatbot to ask questions about software repositories 2) the task of asking development questions on Q&A forums (e.g., Stack Overflow). According to our findings, IBM Watson is the best performing NLU when considering the three aspects (intents classification, confidence scores, and entity extraction). However, the results from each individual aspect show that, in intents classification, IBM Watson performs the best with an F1-measure > 84%, but in confidence scores, Rasa comes on top with a median confidence score higher than 0.91. Our results also show that all NLUs, except for Dialogflow, generally provide trustable confidence scores. For entity extraction, Microsoft LUIS and IBM Watson outperform other NLUs in the two SE tasks. Our results provide guidance to software engineering practitioners when deciding which NLU to use in their chatbots.

cs.SE↗

arXiv2020

On the Threat of npm Vulnerable Dependencies in Node.js Applications

Software vulnerabilities have a large negative impact on the software systems that we depend on daily. Reports on software vulnerabilities always paint a grim picture, with some reports showing that 83% of organizations depend on vulnerable software. However, our experience leads us to believe that, in the grand scheme of things, these software vulnerabilities may have less impact than what is reported. Therefore, we perform a study to better understand the threat of npm vulnerable packages used in Node.js applications. We define three threat levels for vulnerabilities in packages, based on their lifecycle, where a package vulnerability is assigned a low threat level if it was hidden or still unknown at the time it was used in the dependent application (t), medium threat level if the vulnerability was reported but not yet published at t, and high if it was publicly announced at t. Then, we perform an empirical study involving 6,673 real-world, active, and mature open source Node.js applications. Our findings show that although 67.93% of the examined applications depend on at least one vulnerable package, 94.91% of the vulnerable packages in those affected applications are classified as having low threat. Moreover, we find that in the case of vulnerable packages classified as having high threat, it is the application's lack of updating that makes them vulnerable, i.e., it is not the existence of the vulnerability that is the real problem. Furthermore, we verify our findings at different stages of the application's lifetime and find that our findings still hold. Our study argues that when it comes to software vulnerabilities, things may not be as bad as they seem and that considering vulnerability threat is key.

cs.SE↗