Inference of development activities from interaction with uninstrumented applications

Studying developers’ behavior in software development tasks is crucial for designing effective techniques and tools to support developers’ daily work. In modern software development, developers frequently use different applications including IDEs, Web Browsers, documentation software (such as Office Word, Excel, and PDF applications), and other tools to complete their tasks. This creates significant challenges in collecting and analyzing developers’ behavior data. Researchers usually instrument the software tools to log developers’ behavior for further studies. This is feasible for studies on development activities using specific software tools. However, instrumenting all software tools commonly used in real work settings is difficult and requires significant human effort. Furthermore, the collected behavior data consist of low-level and fine-grained event sequences, which must be abstracted into high-level development activities for further analysis. This abstraction is often performed manually or based on simple heuristics. In this paper, we propose an approach to address the above two challenges in collecting and analyzing developers’ behavior data. First, we use our ActivitySpace framework to improve the generalizability of the data collection. ActivitySpace uses operating-system level instrumentation to track developer interactions with a wide range of applications in real work settings. Secondly, we use a machine learning approach to reduce the human effort to abstract low-level behavior data. Specifically, considering the sequential nature of the interaction data, we propose a Condition Random Field (CRF) based approach to segment and label the developers’ low-level actions into a set of basic, yet meaningful development activities. To validate the generalizability of the proposed data collection approach, we deploy the ActivitySpace framework in an industry partner’s company and collect the real working data from ten professional developers’ one-week work in three actual software projects. The experiment with the collected data confirms that with initial human-labeled training data, the CRF model can be trained to infer development activities from low-level actions with reasonable accuracy within and across developers and software projects. This suggests that the machine learning approach is promising in reducing the human efforts required for behavior data analysis.This work was partially supported by NSFC Program (No. 61602403 and 61572426)

Impaired CK1 Delta Activity Attenuates SV40-Induced Cellular Transformation In Vitro and Mouse Mammary Carcinogenesis In Vivo

Simian virus 40 (SV40) is a powerful tool to study cellular transformation in vitro, as well as tumor development and progression in vivo. Various cellular kinases, among them members of the CK1 family, play an important role in modulating the transforming activity of SV40, including the transforming activity of T-Ag, the major transforming protein of SV40, itself. Here we characterized the effects of mutant CK1δ variants with impaired kinase activity on SV40-induced cell transformation in vitro, and on SV40-induced mammary carcinogenesis in vivo in a transgenic/bi-transgenic mouse model. CK1δ mutants exhibited a reduced kinase activity compared to wtCK1δ in in vitro kinase assays. Molecular modeling studies suggested that mutation N172D, located within the substrate binding region, is mainly responsible for impaired mutCK1δ activity. When stably over-expressed in maximal transformed SV-52 cells, CK1δ mutants induced reversion to a minimal transformed phenotype by dominant-negative interference with endogenous wtCK1δ. To characterize the effects of CK1δ on SV40-induced mammary carcinogenesis, we generated transgenic mice expressing mutant CK1δ under the control of the whey acidic protein (WAP) gene promoter, and crossed them with SV40 transgenic WAP-T-antigen (WAP-T) mice. Both WAP-T mice as well as WAP-mutCK1δ/WAP-T bi-transgenic mice developed breast cancer. However, tumor incidence was lower and life span was significantly longer in WAP-mutCK1δ/WAP-T bi-transgenic animals. The reduced CK1δ activity did not affect early lesion formation during tumorigenesis, suggesting that impaired CK1δ activity reduces the probability for outgrowth of in situ carcinomas to invasive carcinomas. The different tumorigenic potential of SV40 in WAP-T and WAP-mutCK1δ/WAP-T tumors was also reflected by a significantly different expression of various genes known to be involved in tumor progression, specifically of those involved in wnt-signaling and DNA repair. Our data show that inactivating mutations in CK1δ impair SV40-induced cellular transformation in vitro and mouse mammary carcinogenesis in vivo

Efficient inverted polymer solar cells employing favourable molecular orientation

The improvement in the power conversion efficiency is a critical issue for polymer-based bulk-heterojunction solar cells (PSCs). Here, we show that high efficiencies of ~10% can be obtained by using a crystalline polymer, PNTz4T, in single-junction inverted cells with the thick active layer measuring ca. 300 nm. The improved performance is likely due to the large population of the polymer crystallites with the face-on orientation and the “favourable” distribution of edge-on and face-on crystallites along the film thickness, as revealed by in-depth studies of the blend films using grazing incidence wide angle X-ray diffraction, which results in the reduction of charge recombination and the efficient charge transport. These results underscore the great promise of PSCs, and raise hopes of achieving even higher efficiencies by materials development and control of molecular ordering