Retrieving curated Stack Overflow Posts of similar project tasks

Software development depends on diverse technologies and methods and as a result, software development teams often handle issues in which team members are not experts. In order to address this lack of expertise, developers typically search for information on web-based Q&A sites such as Stack Overflow, a well-known place to find solutions to specific technology-related problems. Access to these web-based Q&A locations is currently not integrated into the software development environment, and since the associations between software development projects and the supporting sources of known solutions, usually referred to as knowledge, is not explicitly recorded, software developers often need to search for solutions to similar recurring issues multiple times. This lack of integration hinders the reuse of the knowledge obtained, besides not avoiding efforts of search and selection, curation, of this knowledge over and over again. This research aims at proposing a study regarding explicitly associating project elements (such as project tasks) to Stack Overflow posts that have already been curated by developers, and presents a study about Stack Overflow posts suggestions to developers based on similarity of project tasks.O desenvolvimento de software depende de diversas tecnologias e métodos e, como resultado, as equipes de desenvolvimento de software geralmente lidam com problemas em que não são especialistas. Para lidar com a falta de conhecimento, desenvolvedores normalmente procuram informações em sites de perguntas e respostas, como o Stack Overflow, um site usado para encontrar soluções para problemas específicos relacionados à tecnologia. O acesso a esses sites não é integrado ao ambiente de desenvolvimento de software e porque as associações entre os projetos de desenvolvimento de software e as fontes de suporte de soluções conhecidas não são explicitamente registradas. Com isso, desenvolvedores de software podem investir um esforço em procurar soluções para problemas semelhantes várias vezes. Essa falta de integração dificulta o reuso do conhecimento obtido, além de não evitar esforços de busca e seleção, a curadoria, repetidas vezes. Esta pesquisa tem como objetivo realizar um estudo sobre a associação explicita entre elementos do projeto (como tarefas de projeto) a publicações do Stack Overflow que já sofreram curadoria por desenvolvedores, e apresenta um estudo sobre sugestões de publicações do Stack Overflow a desenvolvedores com base na similaridade de tarefas de projeto

Ethics in the mining of software repositories

Research in Mining Software Repositories (MSR) is research involving human subjects, as the repositories usually contain data about developers’ and users’ interactions with the repositories and with each other. The ethics issues raised by such research therefore need to be considered before beginning. This paper presents a discussion of ethics issues that can arise in MSR research, using the mining challenges from the years 2006 to 2021 as a case study to identify the kinds of data used. On the basis of contemporary research ethics frameworks we discuss ethics challenges that may be encountered in creating and using repositories and associated datasets. We also report some results from a small community survey of approaches to ethics in MSR research. In addition, we present four case studies illustrating typical ethics issues one encounters in projects and how ethics considerations can shape projects before they commence. Based on our experience, we present some guidelines and practices that can help in considering potential ethics issues and reducing risks

Ethical Mining – A Case Study on MSR Mining Challenges

Research in Mining Software Repositories (MSR) is research involving human subjects, as the repositories usually contain data about developers’ interactions with the repositories. Therefore, any research in the area needs to consider the ethics implications of the intended activity before starting. This paper presents a discussion of the ethics implications of MSR research, using the mining challenges from the years 2010 to 2019 as a case study to identify the kinds of data used. It highlights problems that one may encounter in creating such datasets, and discusses ethics challenges that may be encountered when using existing datasets, based on a contemporary research ethics framework. We suggest that the MSR community should increase awareness of ethics issues by openly discussing ethics considerations in published articles

Improving Software Project Health Using Machine Learning

In recent years, systems that would previously live on different platforms have been integrated under a single umbrella. The increased use of GitHub, which offers pull-requests, issue trackingand version history, and its integration with other solutions such as Gerrit, or Travis, as well as theresponse from competitors, created development environments that favour agile methodologiesby increasingly automating non-coding tasks: automated build systems, automated issue triagingetc. In essence, source-code hosting platforms shifted to continuous integration/continuousdelivery (CI/CD) as a service. This facilitated a shift in development paradigms, adherents ofagile methodology can now adopt a CI/CD infrastructure more easily. This has also created large,publicly accessible sources of source-code together with related project artefacts: GHTorrent andsimilar datasets now offer programmatic access to the whole of GitHub. Project health encompasses traceability, documentation, adherence to coding conventions,tasks that reduce maintenance costs and increase accountability, but may not directly impactfeatures. Overfocus on health can slow velocity (new feature delivery) so the Agile Manifestosuggests developers should travel light — forgo tasks focused on a project health in favourof higher feature velocity. Obviously, injudiciously following this suggestion can undermine aproject’s chances for success. Simultaneously, this shift to CI/CD has allowed the proliferation of Natural Language orNatural Language and Formal Language textual artefacts that are programmatically accessible:GitHub and their competitors allow API access to their infrastructure to enable the creation ofCI/CD bots. This suggests that approaches from Natural Language Processing and MachineLearning are now feasible and indeed desirable. This thesis aims to (semi-)automate tasks forthis new paradigm and its attendant infrastructure by bringing to the foreground the relevant NLPand ML techniques. Under this umbrella, I focus on three synergistic tasks from this domain: (1) improving theissue-pull-request traceability, which can aid existing systems to automatically curate the issuebacklog as pull-requests are merged; (2) untangling commits in a version history, which canaid the beforementioned traceability task as well as improve the usability of determining a faultintroducing commit, or cherry-picking via tools such as git bisect; (3) mixed-text parsing, to allowbetter API mining and open new avenues for project-specific code-recommendation tools

A New Approach to Memory Partitioning in On-board Spacecraft Software. In Fabrice Kordon and Tullio Vardanega (eds.), Reliable Software Technologies

The current trend to use partitioned architectures in on-board spacecraft software requires applications running on the same computer platform to be isolated from each other both in the temporal and memory domains. Memory isolation techniques currently used in Integrated Modular Avionics for Aeronautics usually require a Memory Management Unit (MMU), which is not commonly available in the kind of processors currently used in the Space domain. Two alternative approaches are discussed in the paper, based on some features of Ada and state-of-the art compilation tool-chains. Both approaches provide safe memory partitioning with less overhead than current IMA techniques. Some footprint and performance metrics taken on a prototype implementation of the most flexible approach are included

Holistic recommender systems for software engineering

The knowledge possessed by developers is often not sufficient to overcome a programming problem. Short of talking to teammates, when available, developers often gather additional knowledge from development artifacts (e.g., project documentation), as well as online resources. The web has become an essential component in the modern developer’s daily life, providing a plethora of information from sources like forums, tutorials, Q&A websites, API documentation, and even video tutorials. Recommender Systems for Software Engineering (RSSE) provide developers with assistance to navigate the information space, automatically suggest useful items, and reduce the time required to locate the needed information. Current RSSEs consider development artifacts as containers of homogeneous information in form of pure text. However, text is a means to represent heterogeneous information provided by, for example, natural language, source code, interchange formats (e.g., XML, JSON), and stack traces. Interpreting the information from a pure textual point of view misses the intrinsic heterogeneity of the artifacts, thus leading to a reductionist approach. We propose the concept of Holistic Recommender Systems for Software Engineering (H-RSSE), i.e., RSSEs that go beyond the textual interpretation of the information contained in development artifacts. Our thesis is that modeling and aggregating information in a holistic fashion enables novel and advanced analyses of development artifacts. To validate our thesis we developed a framework to extract, model and analyze information contained in development artifacts in a reusable meta- information model. We show how RSSEs benefit from a meta-information model, since it enables customized and novel analyses built on top of our framework. The information can be thus reinterpreted from an holistic point of view, preserving its multi-dimensionality, and opening the path towards the concept of holistic recommender systems for software engineering

A FPGA-based architecture for real-time cluster finding in the LHCb silicon pixel detector

The data acquisition system of the LHCb experiment has been substantially upgraded for the LHC Run 3, with the unprecedented capability of reading out and fully reconstructing all proton–proton collisions in real time, occurring with an average rate of 30 MHz, for a total data flow of approximately 32 Tb/s. The high demand of computing power required by this task has motivated a transition to a hybrid heterogeneous computing architecture, where a farm of graphics cores, GPUs, is used in addition to general–purpose processors, CPUs, to speed up the execution of reconstruction algorithms. In a continuing effort to improve real–time processing capabilities of this new DAQ system, also with a view to further luminosity increases in the future, low–level, highly–parallelizable tasks are increasingly being addressed at the earliest stages of the data acquisition chain, using special–purpose computing accelerators. A promising solution is offered by custom–programmable FPGA devices, that are well suited to perform high–volume computations with high throughput and degree of parallelism, limited power consumption and latency. In this context, a two–dimensional FPGA–friendly cluster–finder algorithm has been developed to reconstruct hit positions in the new vertex pixel detector (VELO) of the LHCb Upgrade experiment. The associated firmware architecture, implemented in VHDL language, has been integrated within the VELO readout, without the need for extra cards, as a further enhancement of the DAQ system. This pre–processing allows the first level of the software trigger to accept a 11% higher rate of events, as the ready– made hit coordinates accelerate the track reconstruction, while leading to a drop in electrical power consumption, as the FPGA implementation requires O(50x) less power than the GPU one. The tracking performance of this novel system, being indistinguishable from a full–fledged software implementation, allows the raw pixel data to be dropped immediately at the readout level, yielding the additional benefit of a 14% reduction in data flow. The clustering architecture has been commissioned during the start of LHCb Run 3 and it currently runs in real time during physics data taking, reconstructing VELO hit coordinates on–the–fly at the LHC collision rate

Towards Next Generation Bug Tracking Systems

Although bug tracking systems are fundamental to support virtually any software development process, they are currently suboptimal to support the needs and complexities of large communities. This dissertation first presents a study showing empirical evidence that the traditional interface used by current bug tracking systems invites much noise—unreliable, unuseful, and disorganized information—into the ecosystem. We find that noise comes from, not only low-quality contributions posted by inexperienced users or from conflicts that naturally arise in such ecosystems, but also from the difficulty of fitting the complex bug resolution process and knowledge into the linear sequence of comments that current bug tracking systems use to collect and organize information. Since productivity in bug tracking systems relies on bug reports with accessible and realible information, this leaves contributors struggling to work on and to make sense of the dumps of data submitted to bug reports and, thus, impacting productivity. Next generation bug tracking systems should be more than a tool for exchanging unstructured textual comments. They should be an ecosystem that is tailored for collaborative knowledge building, leveraging the power of the masses to collect reliable and useful information about bugs, providing mechanisms and incentives to verify the validity of such information and mechanisms to organize such information, thus, facilitating comprehension and reasoning. To bring bug tracking systems towards this vision, we present three orthogonal approaches aiming at increasing the usefulness and realiability of contributions and organizing information to improve understanding and reasoning. To improve the usefulness and realibility of contributions we propose the addition of game mechanisms to bug tracking systems, with the objective of motivating contributors to post higher-quality content. Through an empirical investigation of Stack Overflow we evaluate the effects of the mechanisms in such a collaborative software development ecosystem and map a promissing approach to use game mechanisms in bug tracking systems. To improve data organization, we propose two complementary approaches. The first is an automated approach to data organization, creating bug report summaries that make reading and working with bug reports easier, by highlighting the portions of bug reports that expert developers would focus on, if reading the bug report in a hurry. The second approach to improve data organization is a fundamental change on how data is collected and organized, eliminating comments as the main component of bug reports. Instead of comments, users contribute informational posts about bug diagnostics or solutions, allowing users to post contextual comments for each of the different diagnostic iiior solution posts. Our evaluations with real bug tracking system users find that they consider the bug report summaries to be very useful in facilitating common bug tracking system tasks, such as finding duplicate bug reports. In addition, users found that organzing content though diagnostic and solution posts to significanly facilitate reasoning about and searching for relevant information. Finally, we present future directions of work investigating how next generation bug tracking systems could combine the use of the three approaches, such that they benefit from and build upon the results of the other approaches. Next generation bug tracking systems should be more than a tool for exchanging unstructured textual comments. They should be an ecosystem that is tailored for collaborative knowledge building, leveraging the power of the masses to collect reliable and useful information about bugs, providing mechanisms and incentives to verify the validity of such information and mechanisms to organize such information, thus, facilitating comprehension and reasoning. To bring bug tracking systems towards this vision, we present three orthogonal approaches aiming at increasing the usefulness and realiability of contributions and organizing information to improve understanding and reasoning. To improve the usefulness and realibility of contributions we propose the addition of game mechanisms to bug tracking systems, with the objective of motivating contributors to post higher-quality content. Through an empirical investigation of Stack Overflow we evaluate the effects of the mechanisms in such a collaborative software development ecosystem and map a promissing approach to use game mechanisms in bug tracking systems. To improve data organization, we propose two complementary approaches. The first is an automated approach to data organization, creating bug report summaries that make reading and working with bug reports easier, by highlighting the portions of bug reports that expert developers would focus on, if reading the bug report in a hurry. The second approach to improve data organization is a fundamental change on how data is collected and organized, eliminating comments as the main component of bug reports. Instead of comments, users contribute informational posts about bug diagnostics or solutions, allowing users to post contextual comments for each of the different diagnostic iiior solution posts. Our evaluations with real bug tracking system users find that they consider the bug report summaries to be very useful in facilitating common bug tracking system tasks, such as finding duplicate bug reports. In addition, users found that organzing content though diagnostic and solution posts to significanly facilitate reasoning about and searching for relevant information. Finally, we present future directions of work investigating how next generation bug tracking systems could combine the use of the three approaches, such that they benefit from and build upon the results of the other approaches