Developers often search for relevant code examples on the web for their programming tasks. Unfortunately, they face two major problems. First, the search is impaired due to a lexical gap between their query (task description) and the information associated with the solution. Second, the retrieved solution may not be comprehensive, i.e., the code segment might miss a succinct explanation. These problems make the developers browse dozens of documents in order to synthesize an appropriate solution. To address these two problems, we propose CROKAGE (Crowd Knowledge Answer Generator), a tool that takes the description of a programming task (the query) and provides a comprehensive solution for the task. Our solutions contain not only relevant code examples but also their succinct explanations. Our proposed approach expands the task description with relevant API classes from Stack Overflow Q&A threads and then mitigates the lexical gap problems. Furthermore, we perform natural language processing on the top quality answers and then return such programming solutions containing code examples and code explanations unlike earlier studies. We evaluate our approach using 97 programming queries, of which 50% was used for training and 50% was used for testing, and show that it outperforms six baselines including the state-of-art by a statistically significant margin. Furthermore, our evaluation with 29 developers using 24 tasks (queries) confirms the superiority of CROKAGE over the state-of-art tool in terms of relevance of the suggested code examples, benefit of the code explanations and the overall solution quality (code + explanation).

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Abstract-Stack Overflow has become a fundamental element of developer toolset. Such influence increase has been accompanied by an effort from Stack Overflow community to keep the quality of its content. One of the problems which jeopardizes that quality is the continuous growth of duplicated questions. To solve this problem, prior works focused on automatically detecting duplicated questions. Two important solutions are DupPredictor and Dupe. Despite reporting significant results, both works do not provide their implementations publicly available, hindering subsequent works in scientific literature which rely on them. We executed an empirical study as a reproduction of DupPredictor and Dupe. Our results, not robust when attempted with different set of tools and data sets, show that the barriers to reproduce these approaches are high. Furthermore, when applied to more recent data, we observe a performance decay of our both reproductions in terms of recall-rate over time, as the number of questions increases. Our findings suggest that the subsequent works concerning detection of duplicated questions in Question and Answer communities require more investigation to assert their findings.

Stack Overflow has become a fundamental element of developer toolset. Such influence increase has been accompanied by an effort from Stack Overflow community to keep the quality of its content. One of the problems which jeopardizes that quality is the continuous growth of duplicated questions. To solve this problem, prior works focused on automatically detecting duplicated questions. Two important solutions are DupPredictor and Dupe. Despite reporting significant results, both works do not provide their implementations publicly available, hindering subsequent works in scientific literature which rely on them. We executed an empirical study as a reproduction of DupPredictor and Dupe. Our results, not robust when attempted with different set of tools and data sets, show that the barriers to reproduce these approaches are high. Furthermore, when applied to more recent data, we observe a performance decay of our both reproductions in terms of recall-rate over time, as the number of questions increases. Our findings suggest that the subsequent works concerning detection of duplicated questions in Question and Answer communities require more investigation to assert their findings.

Developers often search for relevant code examples on the web for their programming tasks. Unfortunately, they face three major problems. First, they frequently need to read and analyse multiple results from the search engines to obtain a satisfactory solution. Second, the search is impaired due to a lexical gap between the query (task description) and the information in the solution (e.g., code example). Third, the retrieved solution may not be comprehensible, i.e., the code segment might miss a succinct explanation. To address these three problems, we propose CROKAGE (Crowd Knowledge Answer Generator), a tool that takes the description of a programming task (the query) as input and delivers a comprehensible solution for the task. Our solutions contain not only relevant code examples but also their succinct explanations written by human developers. We evaluate and compare our approach against ten baselines, including the state-of-art. We show that CROKAGE outperforms the ten baselines in suggesting relevant solutions for 902 programming tasks (i.e., queries) of three popular programming languages: Java, Python and PHP. Furthermore, we use 24 programming tasks (queries) to evaluate our solutions with 29 developers and confirm that CROKAGE outperforms the state-of-art tool in terms of relevance of the suggested code examples, benefit of the code explanations and the overall solution quality (code + explanation).