Previous work has established both the importance and difficulty of establishing and maintaining adequate software traceability. While it has been shown to support essential maintenance and evolution tasks, recovering traceability links between related software artifacts is a time consuming and error prone task. As such, substantial research has been done to reduce this barrier to adoption by at least partially automating traceability link recovery. In particular, recent work has shown that supervised machine learning can be effectively used for automating traceability link recovery, as long as there is sufficient data (i.e., labeled traceability links) to train a classification model. Unfortunately, the amount of data required by these techniques is a serious limitation, given that most software systems rarely have traceability information to begin with. In this paper we address this limitation of previous work and propose an approach based on active learning, which substantially reduces the amount of training data needed by supervised classification approaches for traceability link recovery while maintaining similar performance.

Previous studies have shown that software traceability, the ability to link together related artifacts from different sources within a project (e.g., source code, use cases, documentation, etc.), improves project outcomes by assisting developers and other stakeholders with common tasks such as impact analysis, concept location, etc. Establishing traceability links in a software system is an important and costly task, but only half the struggle. As the project undergoes maintenance and evolution, new artifacts are added and existing ones are changed, resulting in outdated traceability information. Therefore, specific steps need to be taken to make sure that traceability links are maintained in tandem with the rest of the project. In this paper we address this problem and propose a novel approach called TRAIL for maintaining traceability information in a system. The novelty of TRAIL stands in the fact that it leverages previously captured knowledge about project traceability to train a machine learning classifier which can then be used to derive new traceability links and update existing ones. We evaluated TRAIL on 11 commonly used traceability datasets from six software systems and compared it to seven popular Information Retrieval (IR) techniques including the most common approaches used in previous work. The results indicate that TRAIL outperforms all IR approaches in terms of precision, recall, and F-score.

Text Retrieval (TR) has been widely used to support many software engineering tasks, including bug localization (i.e., the activity of localizing buggy code starting from a bug report). Many studies show TR's effectiveness in lowering the manual effort required to perform this maintenance task; however, the actual usefulness of TR-based bug localization has been questioned in recent studies. These studies discuss (i) potential biases in the experimental design usually adopted to evaluate TRbased bug localization techniques and (ii) their poor performance in the scenario when they are needed most: when the bug report, which serves as the de facto query in most studies, does not contain localization hints (e.g., code snippets, method names, etc.) Fundamentally, these studies raise the question: do bug reports provide sufficient information to perform TR-based localization? In this work, we approach that question from two perspectives. First, we investigate potential biases in the evaluation of TR-based approaches which artificially boost the performance of these techniques, making them appear more successful than they are. Second, we analyze bug report text with and without localization hints using a genetic algorithm to derive a near-optimal query that provides insight into the potential of that bug report for use in TR-based localization. Through this analysis we show that in most cases the bug report vocabulary (i.e., the terms contained in the bug title and description) is all we need to formulate effective queries, making TR-based bug localization successful without supplementary query expansion. Most notably, this also holds when localization hints are completely absent from the bug report. In fact, our results suggest that the next major step in improving TR-based bug localization is the ability to formulate these near-optimal queries.

A wide range of text-based artifacts contribute to software projects (e.g., source code, test cases, use cases, project requirements, interaction diagrams, etc.). Traceability Link Recovery (TLR) is the software task in which relevant documents in these various sets are linked to one another, uncovering information about the project that is not available when considering only the documents themselves. This information is helpful for enabling other tasks such as improving test coverage, impact analysis, and ensuring that system or regulatory requirements are met. However, while traceability links are useful, performing TLR manually is time consuming and fraught with error. Previous work has applied Information Retrieval (IR) and other techniques to reduce the human effort involved; however, that effort remains significant. In this research we seek to take the next step in reducing it by using machine learning (ML) classification models to predict whether a candidate link is valid or invalid without human oversight. Preliminary results show that this approach has promise for accurately recommending valid links; however, there are several challenges that still must be addressed in order to achieve a technique with high enough performance to consider it a viable, completely automated solution.

Traceability Link Recovery (TLR) is an important software engineering task in which a stakeholder establishes links between related items in two sets of software artifacts. Most existing approaches leverage Information Retrieval (IR) techniques, and formulate the TLR task as a retrieval problem, where pairs of similar artifacts are retrieved and presented to a user. These approaches still require significant human effort, as a stakeholder needs to manually inspect the list of recommendations and decide which ones are true links and which ones are false. In this work, we aim to automate TLR by re-imagining it as a binary classification problem. More specifically, our machine learning classification approach is able to automatically classify each link in the set of all potential links as either valid or invalid, therefore circumventing the substantial human effort required by existing techniques.

Context: Since the mid-2000s, numerous recommendation systems based on text retrieval (TR) have been proposed to support software engineering (SE) tasks such as concept location, traceability link recovery, code reuse, impact analysis, and so on. The success of TR-based solutions highly depends on the query submitted, which is either formulated by the developer or automatically extracted from software artifacts. Aim: We aim at predicting the quality of queries submitted to TR-based approaches in SE. This can lead to benefits for developers and for the quality of software systems alike. For example, knowing when a query is poorly formulated can save developers the time and frustration of analyzing irrelevant search results. Instead, they could focus on reformulating the query. Also, knowing if an artifact used as a query leads to irrelevant search results may uncover underlying problems in the query artifact itself. Method: We introduce an automatic query quality prediction approach for software artifact retrieval by adapting NL-inspired solutions to their use on software data. We present two applications and evaluations of the approach in the context of concept location and traceability link recovery, where TR has been applied most often in SE. For concept location, we use the approach to determine if the list of retrieved code elements is likely to contain code relevant to a particular change request or not, in which case, the queries are good candidates for reformulation. For traceability link recovery, the queries represent software artifacts. In this case, we use the query quality prediction approach to identify artifacts that are hard to trace to other artifacts and may therefore have a low intrinsic quality for TR-based traceability link recovery. Results: For concept location, the evaluation shows that our approach is able to correctly predict the quality of queries in 82% of the cases, on average, using very little training data. In the case of traceability recovery, the proposed approach is able to detect hard to trace artifacts in 74% of the cases, on average. Conclusions: The results of our evaluation on applications for concept location and traceability link recovery indicate that our approach can be used to predict the results of a TR-based approach by assessing the quality of the text query. This can lead to saved effort and time, as well as the identification of software artifacts that may be difficult to trace using TR.