A large-scale study on security-related questions on Stack Overflow was presented in Yang2016WhatPosts . Later, to cluster different security-related questions based on their texts, the authors used LDA tuned with a Genetic Algorithm (GA). Within the most prevalent methods used to mine software repositories, we find algorithms such as LDA (Latent Dirichlet Allocation) and many of its variations, LSI (Latent Semantic Indexing), LSA (Latent Semantic Analysis), PLSI (Probabilistic Latent Semantic Indexing), and ICA (Independent Component Analysis) Chen2016ARepositories ; Blei2003LatentAllocation . A survey on the use of topic models when mining software repositories is presented in Chen2016ARepositories . As such, topic modeling has become one of the most used methods to mine software repositories Chen2011ALines . In Table 1, we present a comparison of typical text mining characteristics and purposes, along with how we view topic modeling applied in software development process mining. With the goal of predicting future developer behavior in the IDE and to make better recommendations to developers, Damevski2018PredictingModels used topic models and specifically applied the Temporal Latent Dirichlet Allocation algorithm on two large interaction datasets for two different IDEs, Microsoft Visual Studio and ABB Robot Studio. In this example, use of a new search algorithm depends on the value of the useNewAlgorithm toggle.
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In our approach, the same value for a given attribute will be dynamically computed and concatenated into the original dataset. The definition of process variant emphasizes that process executions in the same group must have the same value for a given attribute, and each process execution belongs uniquely to a process variant. To validate our approach for profiling developers, while controlling for spurious effects, we performed a controlled experiment where, in the realm of a Python programming contest, a group of developers had the same well-defined set of requirements specifications and a well-defined sprint schedule. To do so, we performed a controlled experiment where, in the realm of a Python programming contest, a group of developers had the same well-defined set of requirements specifications and a well-defined sprint schedule. To appraise the impact of individual behaviors in the outcome of a programming task given a group of developers. Being able to group developers with similar behaviors, for instance, based on the time they spent on each activity or working on a specific artifact, is a step forward in that understanding. You may go about the manual process, if you have a working knowledge of web development and skill set for same, but it will call for huge investments of time and personal dedication.
You can also use time management skills to ensure you’re meeting deadlines in this role. Thus, the report was not made in real-time and the management had to be passive. In Section 4, we report our findings. Results: Findings show that we can clearly characterize with a coherent rationale most developers, and distinguish the top performers from the ones with more challenging behaviors. Findings of the preliminary interviews were combined with a literature review to construct the survey questionnaire. To justify the usefulness of collecting IDE events, and provide context to our proposal, we introduce in this section some preliminary definitions required to understand concepts such as development actions, development sessions, development actions repository and development profiles. Therefore, logs containing a sequence of IDE commands/actions can be mined with topic modeling as any other document would be in searching for different topics. Topic modeling is a method for unsupervised classification of documents, by modeling each document as a mixture of topics and each topic as a mixture of words Nguyen2012DuplicateModeling . Process modeling is a persistent topic in the research literature concerned with software development practices. An approach to detect duplicate bug reports, using information retrieval and topic modeling, namely LDA, was presented in Nguyen2012DuplicateModeling . Also you can check out my website for more information. This should not be taken in any way as a bad quality indicator for such CTFsThis is rather the result of the author’s own experience and publicly available information by the date of publication.
Although most boomers are still a long way from thinking about nursing homes, they’ll be encouraged to know that the Wii Fit game systems are even finding their way into those facilities, helping residents do something they never could in their youth — use a video game to stay limber and strong. If so, you may just have found the perfect way to become a shareware millionaire. A systematic literature review on using students as surrogates for professionals can be found in Kotakonda2012Are . Therefore it is worth reviewing the discussion in the literature on using students as surrogates for professionals. The analysis of fingerprints in event logs Taymouri2020BusinessLogs , the discovery of deviating cases using trace clustering Hompes2015DiscoveringClustering and mining of sequences of developers interactions Damevski2017MiningSmells are examples of topics covered by researchers to overcome or mitigate recurrent problems. As such, it is also amenable to statistical analysis like the ones performed in the area known as “text mining”, where natural language processing (NLP) algorithms and analytical methods are used. By understanding the developers’ aspirations and experience (through our natural language processing capabilities), the system recommends the right set of tests for a developer. Applying process mining algorithms on large event logs, containing a significant number of cases and events, usually requires the use of powerful computational systems and, even then, may lead to long processing times. This approach may lead ultimately to the creation of a catalog of software development process smells.
We used events collected from the IDE during development sessions as input for the unsupervised learning techniques and process mining algorithms. This requires analyzing developers’ traces (i.e. executed actions/commands) within the IDE. A study of software logging using topic models, with the aim of understanding the relationship between the topics of a code snippet and the likelihood of a code snippet being logged (i.e. to contain a logging statement) is described in Li2018StudyingModels . In this paper, we describe a novel method to detect different developers’ profiles based on models built from development interactions using n-gram probabilistic language models Jurafsky2020Speech . A language model is a statistical model that allows computing the probability of a sentence, or predict the next word in a sentence for a given language Brown1992Class-BasedLanguage . A programming language or a sequence of development actions in plain English, as seen in Figure 1111This word cloud, where the size of each word is proportional to its relative frequency, was generated from data collected during the validation experiment of our proposed approach., is an artificial language but is expected to follow the same principles of a natural language. This approach used a combination of a word embedding technique and domain-specific relational and categorical knowledge mined from Stack Overflow. A similar approach is presented in Ye2017TheOverflow , based on the structure and dynamics of knowledge network in domain-specific Q&A sites, particularly on Stack Overflow.