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Information Retrieval (IR) methods, and in particular topic models, have recently been used to support essential software engineering (SE) tasks, by enabling software textual retrieval and analysis. In all these approaches, topic models have been used on software artifacts in a similar manner as they were used on natural language documents (e.g., using the(More)
Existing defect prediction models use product or process metrics and machine learning methods to identify defect-prone source code entities. Different classifiers (e.g., linear regression, logistic regression, or classification trees) have been investigated in the last decade. The results achieved so far are sometimes contrasting and do not show a clear(More)
—Information Retrieval (IR) techniques have been used for various software engineering tasks, including the labeling of software artifacts by extracting " keywords " from them. Such techniques include Vector Space Models, Latent Semantic Indexing , Latent Dirichlet Allocation, as well as customized heuristics extracting words from specific source code(More)
Cross-project defect prediction is very appealing because (i) it allows predicting defects in projects for which the availability of data is limited, and (ii) it allows producing generalizable prediction models. However, existing research suggests that cross-project prediction is particularly challenging and, due to heterogeneity of projects, prediction(More)
Information Retrieval (IR) has been widely accepted as a method for automated traceability recovery based on the textual similarity among the software artifacts. However, a notorious difficulty for IR-based methods is that artifacts may be related even if they are not textually similar. A growing body of work addresses this challenge by combining IR-based(More)
The intensive human effort needed to manually manage traceability information has increased the interest in utilising semi-automated traceability recovery techniques. This paper presents a simple way to improve the accuracy of traceability recovery methods based on Information Retrieval techniques. The proposed method acts on the artefact indexing(More)
In this demo we present TraceME (Traceability Management in Eclipse), an Eclipse plug-in, that supports the software engineer in capturing and maintaining traceability links between different types of artifacts. A comparative analysis of the functionalities of the tools supporting traceability recovery highlights that TraceME is the more comprehensive tool(More)
SUMMARY One of the most successful applications of textual analysis in software engineering is the use of Information Retrieval (IR) methods to reconstruct traceability links between software artifacts. Unfortunately, due to the limitations of both the humans developing artifacts and the IR techniques any IR-based traceability recovery method fails to(More)
The research community in Software Engineering and Software Testing in particular builds many of its contributions on a set of mutually shared expectations. Despite the fact that they form the basis of many publications as well as open-source and commercial testing applications, these common expectations and beliefs are rarely ever questioned. For example,(More)
Automated test generation tools have been widely investigated with the goal of reducing the cost of testing activities. However, generated tests have been shown not to help developers in detecting and finding more bugs even though they reach higher structural coverage compared to manual testing. The main reason is that generated tests are difficult to(More)