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Bug reports are essential software artifacts that describe software bugs, especially in open-source software. Lately, due to the availability of a large number of bug reports, a considerable amount of research has been carried out on bug-report analysis, such as automatically checking duplication of bug reports and localizing bugs based on bug reports. To review the work on bug-report analysis, this paper presents an exhaustive survey on the existing work on bug-report analysis. In particular, this paper first presents some background for bug reports and gives a small empirical study on the bug reports on Bugzilla to motivate the necessity for work on bug-report analysis. Then this paper summaries the existing work on bug-report analysis and points out some possible problems in working with bug-report analysis.

When identifying the origin of software bugs, many studies assume that “a bug was introduced by the lines of code that were modified to fix it”. However, this assumption does not always hold and at least in some cases, these modified lines are not responsible for introducing the bug. For example, when the bug was caused by a change in an external API. The lack of empirical evidence makes it impossible to assess how important these cases are and therefore, to which extent the assumption is valid. To advance in this direction, and better understand how bugs “are born”, we propose a model for defining criteria to identify the first snapshot of an evolving software system that exhibits a bug. This model, based on the perfect test idea, decides whether a bug is observed after a change to the software. Furthermore, we studied the model’s criteria by carefully analyzing how 116 bugs were introduced in two different open source software projects. The manual analysis helped classify the root cause of those bugs and created manually curated datasets with bug-introducing changes and with bugs that were not introduced by any change in the source code. Finally, we used these datasets to evaluate the performance of four existing SZZ-based algorithms for detecting bug-introducing changes. We found that SZZ-based algorithms are not very accurate, especially when multiple commits are found; the F-Score varies from 0.44 to 0.77, while the percentage of true positives does not exceed 63%. Our results show empirical evidence that the prevalent assumption, “a bug was introduced by the lines of code that were modified to fix it”, is just one case of how bugs are introduced in a software system. Finding what introduced a bug is not trivial: bugs can be introduced by the developers and be in the code, or be created irrespective of the code. Thus, further research towards a better understanding of the origin of bugs in software projects could help to improve design integration tests and to design other procedures to make software development more robust.

The ladybug, Cryptolaemus montrouzieri (Mulsant) (Coleoptera: Cocccinellidae)(Mulsant)(Coleoptera: Cocccinellidae), is a highly efficient predator in controlling mealybug populations and is considered an effective agent for controlling the papaya mealybugs (Paracoccus marginatus) (Williams & Granara de Willink) (Hemiptera: Pseudococcidae). Various criteria have been proposed for evaluating predator effectiveness, with the consumption rate of prey by individual predators, specifically the functional response, emerging as a common and crucial metric. This study evaluated the functional responses of third- and fourth-instar larvae, as well as male and female adults (<48 h old) of C. montrouzieri to adult females of P. marginatus at 3 different temperatures (22 [degrees]C, 28 [degrees]C, and 35 [degrees]C) with 70% [+ or -] 5% RH and a 12L:12D h photoperiod. Prey densities were 2, 4, 8, 16, 32, 45, or 60 papaya mealybugs per predator for all tests. The response to prey density by third- and fourth-instar larvae or both sexes of adult C. montrouzieri was a type II at all temperatures. The highest attack rate and lowest handling time were estimated at 28 [degrees]C in males and 35 [degrees]C in females, respectively. The highest daily prey consumption rate occurred at 35 [degrees]C in both the immature and adult stages of C. montrouzieri.These findings support the potential of C. montrouzieri in controlling the papaya mealybug, especially in tropical and subtropical regions, given its search efficiency at high temperatures tested in this study. However, additional field investigations are needed to ascertain the control efficacy of C. montrouzieri for this mealybug in biocontrol programs. Key words: handling time, mealybug destroyer, papaya mealybug, searching efficiency

Software development teams need to deal with several open reports of critical bugs to be addressed urgently and simultaneously. The management of these bugs is a complex problem due to the limited resources and the deadlines-pressure. Most of the existing studies treated bug reports in isolation when assigning them to developers. Thus, developers may spend considerable cognitive efforts moving between completely unrelated bug reports thus not sharing any common files to be inspected. In this paper, we propose an automated bugs triage approach based on the dependencies between the open bug reports. Our approach starts by localizing the files to be inspected for each of the pending bug reports. We defined the dependency between two bug reports as the number of common files to be inspected to localize the bugs. Then, we adopted multi-objective search to rank the bug reports for programmers based on both their priorities and the dependency between them. We evaluated our approach on a set of open source programs and compared it to the traditional approach of considering bug reports in isolation based mainly on their priority. The results show a significant time reduction of over 30% in localizing the bugs simultaneously comparing to the traditional bugs prioritization technique based on only priorities.

ContextThe SZZ algorithm is the de facto standard for labeling bug fixing commits and finding inducing changes for defect prediction data. Recent research uncovered potential problems in different parts of the SZZ algorithm. Most defect prediction data sets provide only static code metrics as features, while research indicates that other features are also important.ObjectiveWe provide an empirical analysis of the defect labels created with the SZZ algorithm and the impact of commonly used features on results.MethodWe used a combination of manual validation and adopted or improved heuristics for the collection of defect data. We conducted an empirical study on 398 releases of 38 Apache projects.ResultsWe found that only half of the bug fixing commits determined by SZZ are actually bug fixing. If a six-month time frame is used in combination with SZZ to determine which bugs affect a release, one file is incorrectly labeled as defective for every file that is correctly labeled as defective. In addition, two defective files are missed. We also explored the impact of the relatively small set of features that are available in most defect prediction data sets, as there are multiple publications that indicate that, e.g., churn related features are important for defect prediction. We found that the difference of using more features is not significant.ConclusionProblems with inaccurate defect labels are a severe threat to the validity of the state of the art of defect prediction. Small feature sets seem to be a less severe threat.

The invasive Halyomorpha halys is a serious pest for several fruit trees, causing millions of dollars of crop damage every year across the world’s major fruit-growing regions. Once established in an orchard, H. halys quickly becomes the predominant stink bug species and is a major season-long pest. Annual increases in the population size of H. halys have resulted in increased pest pressure and a growing risk of severe crop damage. Reliable monitoring is indispensable for H. halys control and management, providing comprehensive information on the seasonality of pest population dynamics, abundance, and interaction with the environment, and is essential for the successful implementation of integrated pest management (IPM) strategies to prevent crop damage. Our study followed the seasonal population dynamics of H. halys in three apple orchards in the Goriška region of western Slovenia over the period 2019–2021. Pherocon[sup.®] Dual Panel Adhesive Traps (Trece Inc.) and pyramidal Rescue[sup.®] Stink Bug Traps, both baited with Trécé lures (two-component H. halys aggregation pheromone + pheromone synergist), were used to monitor H. halys adults and nymphs weekly from late March to the end of November. Captures taken with both types of trap clearly describe the seasonal dynamics of H. halys, with the first occurrence of overwintering adults in April and May, and with two peak occurrences in adults, in the middle of summer and in the beginning of autumn, corresponding to the appearance of two generations per year in the study area. The growing trap captures observed during the 3-year monitoring period suggest that H. halys was only recently introduced to the area and that natural enemies have not yet been fully recruited. Pyramid traps captured significantly more adults and nymphs than clear sticky traps and provided accurate monitoring of H. halys life stages throughout the season. Regardless of the lower trap catches of adults and juveniles, clear sticky traps clearly displayed H. halys seasonal dynamics pattern. Therefore, their use is recommended as an early detection tool in areas where pests are not yet present, or in areas with small H. halys populations. Halyomorpha halys adult trap captures were higher in Šempeter orchards, within areas of great landscape diversity and a large share of urban land. The seasonal dynamics of H. halys over the 3-year period were closely related to weather conditions, with temperature and relative humidity as the major factors affecting population growth.

Software development teams generally welcome any effort to expose bugs in their code base. In this work, we build on the hypothesis that mobile apps from the same category (e.g., two web browser apps) may be affected by similar bugs in their evolution process. It is therefore possible to transfer the experience of one historical app to quickly find bugs in its new counterparts. This has been referred to as collaborative bug finding in the literature. Our novelty is that we guide the bug finding process by considering that existing bugs have been hinted within app reviews. Concretely, we design the BugRMSys approach to recommend bug reports for a target app by matching historical bug reports from apps in the same category with user app reviews of the target app. We experimentally show that this approach enables us to quickly expose and report dozens of bugs for targeted apps such as Brave (web browser app). BugRMSys ’s implementation relies on DistilBERT to produce natural language text embeddings. Our pipeline considers similarities between bug reports and app reviews to identify relevant bugs. We then focus on the app review as well as potential reproduction steps in the historical bug report (from a same-category app) to reproduce the bugs. Overall, after applying BugRMSys to six popular apps, we were able to identify, reproduce and report 20 new bugs: among these, 9 reports have been already triaged, 6 were confirmed, and 4 have been fixed by official development teams.

Bug localization is the automated process of finding the possible faulty files in a software project. Bug localization allows developers to concentrate on vital files. Information retrieval (IR)-based approaches have been proposed to assist automatically identify software defects by using bug report information. However, some bug reports that are not semantically related to the relevant code are not helpful to IR-based systems. Running an IR-based reporting system can lead to false-positive results. In this paper, we propose a classification model for classifying a bug report as either uninformative or informative. Our approach helps to lower false positives and increase ranking performances by filtering uninformative information before running an IR-based bug location system. The model is based on implicit features learned from bug reports that use neural networks and explicit features defined manually. We test our proposed model on three open-source software projects that contain over 9000 bug reports. The results of the evaluation show that our model enhances the efficiency of a developed IR-based system in the trade-off between precision and recall. For implicit features, our tests with comparisons show that the LSTM network performs better than the CNN and multilayer perceptron with respect to the F -measurements. Combining both implicit and explicit features outperforms using only implicit features. Our classification model helps improve precision in bug localization tasks when precision is considered more important than recall.

An invasive mealybug (Hemiptera: Coccomorpha: Pseudococcidae) was first detected and identified in Florida in 2019 as Phenacoccus sisymbriifolium Granara de Willink. This species was described from Uruguay in 2007 on Solanum sisymbriifolium Lam. (Solanaceae) and Florida specimens largely matched the description. However, new morphological and molecular evidence supports that this species is Phenacoccus miruku Tanaka & Choi, recently described from Japan in 2022. P. miruku is presumed Neotropical or Nearctic in origin and invasive in Japan. We discuss the issues around the taxonomic identities of these species and for each give diagnoses. An available list of host plants, a current distribution map, notes on ecological associates, images of live specimens in the field, and a key to the species of Phenacoccus in Florida also are provided. Since its detection, this mealybug has been widely found throughout 20 Florida counties with a continuously expanding host list. It is currently most common on roadside weeds such as Bidens alba (L.) DC. and Ambrosia artemisiifolia L. (Asteraceae) but it has recently been identified on cultivated crops such as tomato (Solanum lycopersicum L.), eggplant (Solanum melongena L.), naranjilla (Solanum quitoense Lam.), peppers (Capsicum L.) (Solanaceae), and sweet potato (Ipomoea batatas L.) (Convolvulaceae). This paper serves to provide information on this emerging mealybug pest, give resources for its identification, and facilitate detection and management. Keywords: Coccoidea; Sternorrhyncha; invasive species; scale insect; morphology Una cochinilla invasora (Hemiptera: Coccomorpha: Pseudococcidae) fue detectada e identificada por primera vez en Florida en 2019 como Phenacoccus sisymbriifolium Granara de Willink. Esta especie fue descrita en Uruguay en 2007 en Solanum sisymbriifolium Lam. (Solanaceae) y espec menes de Florida coincid an en gran medida con esta descripci n. Sin embargo, nueva evidencia morfol gica y molecular respalda que esta especie es Phenacoccus miruku Tanaka & Choi, descrita recientemente en Jap n en 2022. Se presume que P. miruku es de origen neotropical o ne rtico e invasivo en Jap n. En este estudio, discutimos temas relacionados con las identidades taxon micas de estas especies y damos diagn sticos para cada una. Tambi n se proporciona una lista disponible de plantas hospedantes, un mapa de distribuci n actual, notas sobre acompa antes ecol gicos, im genes de espec menes vivos en el campo y una clave para las especies de Phenacoccus en Florida. Desde su detecci n, esta cochinilla se ha encontrado en 20 condados de Florida con una lista de hu spedes en continua expansi n. Actualmente es m s com n en las malezas al borde de las carreteras con Bidens alba (L.) DC. y Ambrosia artemisiifolia L. (Asteraceae) pero recientemente se ha identificado en cultivos como tomate (Solanum lycopersicum L.), berenjena (Solanum melongena L.), naranjilla (Solanum quitoense Lam.), pimiento (Capsicum L.) (Solanaceae) y batata (Ipomoea batatas L.) (Convolvulaceae). Este documento sirve para proporcionar informaci n sobre esta emergente plaga de cochinilla, brindar antecedentes para su identificaci n, y facilitar su detecci n y manejo. Palabras Clave: Coccoidea; esternorrincha; especies invasivas; insecto escamoso; morfolog a