Business rules are an important part of the requirements of software systems that are meant to support an organization. These rules describe the operations, definitions, and constraints that apply to the organization. Within the software system, business rules are often translated into constraints on the values that are required or allowed for data, called data constraints. Business rules are subject to frequent changes, which in turn require changes to the corresponding data constraints in the software. The ability to efficiently and precisely identify where data constraints are implemented in the source code is essential for performing such necessary changes.

Automated text-retrieval-based bug localization (TRBL) techniques normally use the full text of a bug report to formulate a query and retrieve parts of the code that are buggy. Previous research has shown that reducing the size of the query increases the effectiveness of TRBL. On the other hand, researchers also found improvements when expanding the query (i.e., adding more terms). In this paper, we bring these two views together to reformulate queries for TRBL. Specifically, we improve discourse-based query reduction strategies, by adopting a combinatorial approach and using task phrases from bug reports, and combine them with a state-of-the-art query expansion technique, resulting in 970 query reformulation strategies. We investigate the benefits of these strategies for localizing buggy code elements and define a new approach, called Qrex, based on the most effective strategy. We evaluated the reformulation strategies, including Qrex, on 1,217 queries from different software systems to retrieve buggy code artifacts at three code granularities, using five state-of-the-art automated TRBL approaches. The results indicate that Qrex increases TRBL effectiveness by 4% - 12.6%, compared to applying query reduction and expansion in isolation, and by 32.1%, compared to the no-reformulation baseline.

When bugs are reported, one important task is to check if they are new or if they were reported before. Many approaches have been proposed to partially automate duplicate bug report detection, and most of them rely on text retrieval techniques, using the bug reports as queries. Some of them include additional bug information and use complex retrieval- or learning-based methods. In the end, even the most sophisticated approaches fail to retrieve duplicate bug reports in many cases, leaving the bug triagers to their own devices. We argue that these duplicate bug retrieval tools should be used interactively, allowing the users to reformulate the queries to refine the retrieval. With that in mind, we are proposing three query reformulation strategies that require the users to simply select from the bug report the description of the software's observed behavior and/or the bug title, and combine them to issue a new query. The paper reports an empirical evaluation of the reformulation strategies, using a basic duplicate retrieval technique, on bug reports with duplicates from 20 open source projects. The duplicate detector failed to retrieve duplicates in top 5-30 for a significant number of the bug reports (between 34% and 50%). We reformulated the queries for a sample of these bug reports and compared the results against the initial query. We found that using the observed behavior description, together with the title, leads to the best retrieval performance. Using only the title or only the observed behavior for reformulation is also better than retrieval with the initial query. The reformulation strategies lead to 56.6%-78% average retrieval improvement, over using the initial query only.

Text Retrieval (TR)-based approaches for bug localization rely on formulating an initial query based on a bug report. Often, the query does not return the buggy software artifacts at or near the top of the list (i.e., it is a low-quality query). In such cases, the query needs reformulation. Existing research on supporting developers in the reformulation of queries focuses mostly on leveraging relevance feedback from the user or expanding the original query with additional information (e.g., adding synonyms). In many cases, the problem with such lowquality queries is the presence of irrelevant terms (i.e., noise) and previous research has shown that removing such terms from the queries leads to substantial improvement in code retrieval. Unfortunately, the current state of research lacks methods to identify the irrelevant terms. Our research aims at addressing this problem and our conjecture is that reducing a low-quality query to only the terms describing the Observed Behavior (OB) can improve TR-based bug localization. To verify our conjecture, we conducted an empirical study using bug data from 21 open source systems to reformulate 451 low-quality queries. We compare the accuracy achieved by four TR-based bug localization approaches at three code granularities (i.e., files, classes, and methods), when using the complete bug reports as queries versus a reduced version corresponding to the OB only. The results show that the reformulated queries improve TR-based bug localization for all approaches by 147.4% and 116.6% on average, in terms of MRR and MAP, respectively. We conclude that using the OB descriptions is a simple and effective technique to reformulate low-quality queries during TR-based bug localization.

Many software comprehension tasks depend on how stakeholders textually describe their problems. These textual descriptions are leveraged by Text Retrieval (TR)-based solutions to more than 20 software engineering tasks, such as duplicate issue detection. The common assumption of such methods is that text describing the same issue in multiple places will have a common vocabulary. This paper presents an empirical study aimed at verifying this assumption and discusses the impact of the common vocabulary on duplicate issue detection. The study investigated 13K+ pairs of duplicate bug reports and Stack Overflow (SO) questions. We found that on average, more than 12.2% of the duplicate pairs do not have common terms. The other duplicate issue descriptions share, on average, 30% of their vocabulary. The good news is that these duplicates have significantly more terms in common than the non-duplicates. We also found that the difference between the lexical agreement of duplicate and non-duplicate pairs is a good predictor for the performance of TR-based duplicate detection.

Current trends in robotic cardiac surgery presage for allowing physiological motion compensation in beating-heart surgery. However, interacting with fast moving soft organs by means of stiff instruments/robots is challenging. This paper concerns comanipulation with a hand-held instrument, the goal being to allow the surgeon to perform low frequency motions that correspond to the surgical task while a distal part of the instrument actively moves in synchronism with the heart motion in order to guarantee that the contact is maintained. This paper explores the difficulties of implementing low-impedance control on a novel hand-held motion compensation instrument. A force feedback control strategy is proposed and evaluated experimentally on a simulated surgical scene. Taking advantage of the sensory capacities of the prototype presented, a successful modulation of the dynamics of interaction is reached. Conclusive results on the performances of the system and possibilities of future improvements are given.